DigitBite

digitbite

Trading. It brings with it some of the highest highs when you ride a rocket to the moon and the lowest lows when you watch your portfolio bleed. Crypto What I’d like to provide to you today, are some things I wish I had known when I first got started, to hopefully shed some light about the trading world and maybe, just maybe, save you from some of the things I learned the hard way. What kind of trader are you? What the hell am I doing? This is a question of self-reflection. You need to honestly look within yourself and determine what kind of trader you want to be. A lot of people fantasize about the Hollywood day-traders and the huge scores they make. Honestly, Day-trading is extremely stressful, and you can find yourself losing your money fast if you do not have time to babysit the market and especially if you do not know what you are doing. There are three different types of traders: · (you make a daily trade or more), Day Trader · (you make a trade every 2–7 days, sometimes longer if that is what the trend requires). Swing Trader · (buys the dips, holds long term). Investor All three types are valid ways to make money but require different strategies. You may find yourself as a blend of these. Personally, I fall into the Swing Trader and Investor category. Sometimes the best trade is one not made. Over trading is one of the most common mistakes of new traders. Let the trade come to you and know what you are getting yourself into. If you cannot answer you made that trade, It is ok to not make a trade daily. why you are literally gambling. Market Psychology is extremely powerful. (TA) is a powerful tool that can help you make educated decisions, but know right away, it is not a crystal ball. Technical Analysis Aw Yeah. $DOGE is heading to the moon. I’d like to point out that FUD (Fear, Uncertainty, Doubt) and FOMO (Fear Of Missing Out) are powerful and even the most textbook chart can be broken causing a . Always practice risk management and utilize . extremely Black Swan event stop losses Understand too what an echo chamber is. If you drop yourself into a coin’s official Telegram channel or are speaking to the guy named “(insert coin here) Whale” they have a personal stake in wanting that coin to go up. I was burned when I first started trading by this when I would hear vast numbers of people in a group for a coin saying “This coin is going to $1 USD!” When you see enough people yelling it, it reinforces it in your mind and you start to believe it. FOMO starts to kick in. With that, It isn’t that hard. Which leads into: learn how to calculate a coin’s price. How to calculate a coin’s price. So DigitByte ($DGB), is the coin I mentioned before that I was burned on. I was told it would be “$1 USD by the end of the year!” I did not know how coin’s prices were calculated, or how worked and blindly threw my money in at the top of its . I paid dearly for this. market caps market cycle Look at the price in the above screenshot. This is calculated by the over all market cap ($563,193,234) divided by the circulating supply (9,739,480,205 DGB). This comes out to the price we see above. You can find all of the coin market caps . here This means, for $DGB to be $1 USD, you would need a $9,739,480,205 USD market cap for that to happen. Is that realistic for your coin of choice? Ensure you fully understand the potential of the coin you are involved with and set realistic expectations. Beware of Twitter, your best friend and worst enemy. I mean come on, it’s the internet. Twitter can be a wonderful source of information as well as a terrible one. Keep in mind, 99.9% of people who are posting charts on twitter have already placed their positions, made their buys, and are now attempting to influence the market into the direction they desire. That guy who showed you (insert coin here) is going to moonshot? They’ve already bought in and if they’re in a group, informed them too. What indicators did that person use to come to that conclusion? Don’t know what that particular indicator is? Ask. Then look it up. Research it. Type that topic in followed with “Investopedia”. That’s what I do still even today. I won’t attempt to teach you TA because there are many people who have already prepared those lessons better than I could. See free crypto lessons to get started on the right path. Question everything. Google CryptoCred’s here (DYOR) That guy who just told you the market is going to the moon; do you even know who they are? How long have they been trading? Most twitter traders are a cartoon avatar. You have no clue who they are. Can you look your friends in the face and say, “I bought this coin because a Doge in a Navy Uniform said so.”? Do your own research. The number of followers someone has does not equal level of credibility or experience. Good looking, but not a licensed adviser. It’s ok to ask questions! Most twitter personalities would be happy to point you in the right direction or explain their charts if you’re asking “how” and not “Target Sir?” I ignore target questions completely because for starters, and at the end of the day, I don’t want to see the torches and pitchforks when I am wrong. Guess what. I do not have a 100% success rate and have been rekt plenty of times. Anyone who claims they haven’t been rekt are liars, and anyone who thinks they are immune to being rekt are fools. I am not a licensed financial adviser “TaRgEt SiR?” General Tips. . There will always be another rocket if you missed it. . On the other side, that coin you bought is now bleeding, is the team legitimate and does the coin have a real use? It will likely come back in time if you’re holding it. There are exceptions, but overall, holding through storms isn’t a bad option (this is not applicable during a bear market). I also started making more when I found great positions on coins, bought and held until they had their day. $DOGE was an example of this when I bought it at 15–17 sats and held it for about 3 months before selling it 100+ sats. Chasing pumps will bleed you dry. Slow down Breathe If you have a good position on a coin, you will worry much less. Average day of trading in crypto. . Is your goal to get more BTC or to invest in a specific coin? Keep that in mind when building your portfolio. If your goal is to gain more BTC, be careful of becoming too loyal to a coin. Having altcoins is NOT the same as having BTC Always keep an eye on BTC. It will destroy your alt parties very quickly if you’re not watching. Bitcoin is King. . Get your long-term, non-trading coins off the exchange. How are you going to feel if that exchange ? Invest in a hard-wallet suddenly shuts down and all those coins are lost and not SMS, but a device based authenticator (like Google Authenticator). . This is your best defense at stopping your account from getting compromised. Use different passwords for different exchanges. Yes, it may be a pain, but would you rather lose your coins? USE TWO-FACTOR AUTHENTICATION SMS Authentication has been hacked before Go to the site and bookmark it. Use this instead of Googling for it. and paying for ads to push them to the top of Google results. Use bookmarks for exchange sites. Phishing sites have been found spoofing exchanges Never trade alone. When trading alone, you do not have the luxury of bouncing questions, charts, and lessons learned off each other. Some people have negative cogitations about paid groups while others praise them to the moon. If you are going to join a paid group, I recommend you do with a group that is education based and not a signals group. You to learn how to properly read these markets and protect your own interests. What are you going to do if that group shuts down? At the end of the day, you’re going to pay one way or the other. You can pay a group to learn, pay the markets in mistakes and through personal experience, or a little of both. Personally, when I got started, I signed up with a paid group to learn and don’t regret it for a moment. I wouldn’t be where I am today if I hadn’t done so. highly need Even if you do not decide to join a paid education group, join one of the many free groups out there, but again, beware of the echo chambers and pump groups. A paid group usually cuts the noise down and you’ll find more serious traders/investors. Find your pack. Conclusion Patience is key in crypto trading and there are a lot of moving parts. Do not be afraid to miss a trade as this environment is so rich with opportunities to make money. Keep in mind, it is easy to make money in this market but much harder to keep it. Any open trade is not yours. Do not let greed drive you and slowly sell out of your position as it moons. Again, You can follow me here: there will always be another rocket to catch! Twitter: @JoshMcGruff Instagram: @JoshMcGruff TradingView: https://www.tradingview.com/u/JoshMcGruff/ You can find me also in our free Crypto Trading Discord group — . Bitcoin Masons Buckle up! It’s a wild ride!

Things I wish I knew when I started Crypto Trading

The Talking Box: A CVSD Audio Digitizer and Playback Device

টকিং বক্স: একটি CVSD অডিও ডিজিটাইজার এবং প্লেব্যাক ডিভাইস

Die Talking Box: Ein CVSD-Audio-Digitalisierungs- und Wiedergabegerät

The Talking Box: un dispositivo de reproducción y digitalizador de audio CVSD

The Talking Box : un numériseur audio et un appareil de lecture CVSD

टॉकिंग बॉक्स: एक CVSD ऑडियो डिजिटाइज़र और प्लेबैक डिवाइस

Talking Box: CVSD オーディオ デジタイザーおよび再生デバイス

Talking Box: CVSD 오디오 디지타이저 및 재생 장치

The Talking Box: um digitalizador de áudio CVSD e dispositivo de reprodução

Говорящий ящик: устройство для оцифровки и воспроизведения звука CVSD

Konuşan Kutu: CVSD Ses Sayısallaştırıcı ve Oynatma Cihazı

Hộp nói: Thiết bị phát lại và số hóa âm thanh CVSD

Talking Box：CVSD 音频数字化仪和播放设备

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Conclusion & Future Work and References

Authors:
(1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu;
(2) Eric Bier, Palo Alto Research Center, USA bier@parc.com;
(3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com;
(4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov;
(5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References 4 Experiments We evaluated our approach to capturing 3D scans using AR headsets. We compared the floor plan dimensions with actual building dimensions and provided intermediate results: floor plans and 3D models. We also calculated the time taken for our algorithm steps. To demonstrate the approach robustness, we evaluated using multiple building types, including commercial buildings B1 and B3 and a residential building B3. Additionally, we validated our floor plan generation on the Matterport2D dataset [25]. Scanned Data Analysis We evaluated the precision of floor plan generation by comparing the actual dimensions of the rooms with the computed floor plan. Figure 10(a) illustrates the measurement of the building, which was scanned twice with our AR. We call these scans S1 and S2 (see figure 10(b) and figure 10(c)) For each scan, floor plans are computed and the dimensions are computed using geometric modeling software, Rhino [17]. We then compared the computed dimensions with the actual room dimensions as shown in Table 1. These results show the applicability of our approach to many building types. Our method can compute a floor plan even from relatively incomplete mesh data. With a higher quality HoloLens scan, the resulting floor plan is more precise. Figure 11 displays S1 results: both types of floor plan and the 3D model. Orienting floor and walls We must orient the mesh properly. Spherical k-means is compute intensive so we optimize it to get good performance. In Figure 4, we see the mesh of B1 before and after alignment, which took 12.4 seconds, of which 10.6 were spent aligning walls using spherical k-means. Partitioning into stories We can detect a multi-story building and divide it into stories with an additional step. The algorithm projects triangles onto the positive y-axis and creates a histogram showing horizontal peaks. By analyzing the peaks in the histogram, we can determine the number of stories. Figures 5 and 12 show a 2-story residential building and a multi-story model from the Matterport3D dataset [25] that were partitioned into stories. Finding planar walls To generate a drafting-style floor plan, we eliminate details and identify planar walls. The modified DBSCAN algorithm is the most time-consuming step. In the model of Figure 13, with 79,931 vertices and 134,235 faces, it took 27.4 seconds to prepare the data and run DBSCAN and an additional 3.79 seconds to construct flat walls from the generated clusters. For the residential building of Figure 14, with 173,941 vertices and 285,840 faces, it took 76 seconds to prepare and run DBSCAN and 23.36 seconds to compute flat walls. The results for a Matterport3D model appear in Figure 15. Generating the floor plan The final step of our floor plan generation is to slice the mesh at different heights and superimpose the slices. Figures 13, 14, and 15 show floor plans generated using our approach. We conducted experiments to evaluate the effects of changing graphical settings when rendering pen-and-ink floor plans. Each setting consists of a different combination of line segment opacity and slice count. We found that an opacity setting of 0.5 produced a floor plan that met our expectations. We also found that a floor plan with 100 slices provided a good balance between level of detail and clutter reduction. Optimal numbers will depend on use case. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Authors: Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Abstract and Intro Related Work Related Work Methodology Methodology Experiments Experiments Conclusion & Future work and References Conclusion & Future work and References 4 Experiments We evaluated our approach to capturing 3D scans using AR headsets. We compared the floor plan dimensions with actual building dimensions and provided intermediate results: floor plans and 3D models. We also calculated the time taken for our algorithm steps. To demonstrate the approach robustness, we evaluated using multiple building types, including commercial buildings B1 and B3 and a residential building B3. Additionally, we validated our floor plan generation on the Matterport2D dataset [25]. Scanned Data Analysis We evaluated the precision of floor plan generation by comparing the actual dimensions of the rooms with the computed floor plan. Figure 10(a) illustrates the measurement of the building, which was scanned twice with our AR. We call these scans S1 and S2 (see figure 10(b) and figure 10(c)) For each scan, floor plans are computed and the dimensions are computed using geometric modeling software, Rhino [17]. We then compared the computed dimensions with the actual room dimensions as shown in Table 1. These results show the applicability of our approach to many building types. Scanned Data Analysis Our method can compute a floor plan even from relatively incomplete mesh data. With a higher quality HoloLens scan, the resulting floor plan is more precise. Figure 11 displays S1 results: both types of floor plan and the 3D model. Orienting floor and walls We must orient the mesh properly. Spherical k-means is compute intensive so we optimize it to get good performance. In Figure 4, we see the mesh of B1 before and after alignment, which took 12.4 seconds, of which 10.6 were spent aligning walls using spherical k-means. Orienting floor and walls Partitioning into stories We can detect a multi-story building and divide it into stories with an additional step. The algorithm projects triangles onto the positive y-axis and creates a histogram showing horizontal peaks. By analyzing the peaks in the histogram, we can determine the number of stories. Figures 5 and 12 show a 2-story residential building and a multi-story model from the Matterport3D dataset [25] that were partitioned into stories. Partitioning into stories Finding planar walls To generate a drafting-style floor plan, we eliminate details and identify planar walls. The modified DBSCAN algorithm is the most time-consuming step. In the model of Figure 13, with 79,931 vertices and 134,235 faces, it took 27.4 seconds to prepare the data and run DBSCAN and an additional 3.79 seconds to construct flat walls from the generated clusters. For the residential building of Figure 14, with 173,941 vertices and 285,840 faces, it took 76 seconds to prepare and run DBSCAN and 23.36 seconds to compute flat walls. The results for a Matterport3D model appear in Figure 15. Finding planar walls Generating the floor plan The final step of our floor plan generation is to slice the mesh at different heights and superimpose the slices. Figures 13, 14, and 15 show floor plans generated using our approach. Generating the floor plan We conducted experiments to evaluate the effects of changing graphical settings when rendering pen-and-ink floor plans. Each setting consists of a different combination of line segment opacity and slice count. We found that an opacity setting of 0.5 produced a floor plan that met our expectations. We also found that a floor plan with 100 slices provided a good balance between level of detail and clutter reduction. Optimal numbers will depend on use case. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Experiments

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Methodology

Authors:
(1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu;
(2) Eric Bier, Palo Alto Research Center, USA bier@parc.com;
(3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com;
(4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov;
(5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References 2 Related Work Floor plans are crucial for many applications. Software approaches to floor plan creation depend on data availability and data format. Our work builds on previous research on data collection and floor plan computation. Data collection. Indoor environments can be captured in many formats, including RGBD images, point clouds, and triangle meshes. Zhang et al. [29] uses panoramic RGBD images as input and reconstructs geometry using structure grammars, while [21] uses a 3D scan to extract plane primitives and generates models using heuristics. A single image is used in some deep learning methods [8,11,13,14,30] to generate cuboid-based layouts for a single room. Detailed semi-constrained floor plan computations for a complete house require processing a 3D scan of the house [15]; the complete scan increases accuracy, but increases computing requirements and time. Pintore and Gobbetti [23] proposed a technique to create floor plans and 3D models using an Android device camera, leveraging sensor data and statistical techniques. Chen et al. [7] introduced an augmented reality system utilizing the Microsoft Hololens for indoor layout assessment, addressing intuitive evaluation and efficiency challenges. In our approach, we begin with a triangle mesh from a HoloLens 2, using its Spatial Mapping software [18], which has been surveyed by Weinmann et al. [27]. Floor plan computations Early methods [1,3,22,28] relied on image processing techniques, such as histograms and plane fitting, to create floor plans from 3D data. While [22] creates a floor plan by detecting vertical planes in a 3D point cloud, [3] uses planar structure extraction to create floor plans. These techniques rely on heuristics and were prone to failure due to noise in the data. There has been much progress in floor plan computation using graphical models [4,9,12]. Such models [10] are also used to recover layouts and floor plans from crowd-sourced images and location data. One interactive tool [16] creates desirable floorplans by conforming to design constraints. Pintore et al. [24] characterizes several available input sources (including the triangle meshes that we use) and output models and discusses the main elements of the reconstruction pipeline. It also identifies several systems for producing floor plans, including FloorNet [15], and Floor-SP [6]. Monszpart et al. [19] introduced an algorithm that exploits the observation that distant walls are generally parallel to identify dominant wall directions using k-means. Our approach also utilizes k-means, but does so to identify walls in all directions, not just the dominant ones. Cai et al. [5] uses geometric priors, including point density, indoor area recognition, and normal information, to reconstruct floorplans from raw point clouds. In contrast to Arikan et al. [2], which employed a greedy algorithm to find plane normal directions and fit planes to points with help from user interaction, our approach is automatic. It also differs from the work of [20], which focuses on removing clutter and partitioning the interior into a 3D cell complex; our method specifically divides the building into separate walls. Our work is related to [22] and [26]. In [22], floor plan generation starts with a laser range data point cloud, followed by floor and ceiling detection using a height histogram. The remaining points are projected onto a ground plane, where a density histogram and Hough transform are applied to generate the line segments that form a floor plan. In projecting to 2D, their method risks losing information that may be useful for creating 3D models or detailed floor plans. Similarly, [26] utilizes a histogram-based approach to detect ceilings and floors. Their method involves identifying taller wall segments to create a 2D histogram, and then employing heuristics based on histogram point density to compute the floor plan. Our approach differs from [22] and [26] by aligning the mesh with global coordinate axes and not relying on laser data or a point cloud. Working primarily with 3D data throughout the pipeline, it benefits from enhanced information and generates both a 3D model and a floor plan. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Authors: Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Abstract and Intro Related Work Related Work Methodology Methodology Experiments Experiments Conclusion & Future work and References Conclusion & Future work and References 2 Related Work Floor plans are crucial for many applications. Software approaches to floor plan creation depend on data availability and data format. Our work builds on previous research on data collection and floor plan computation. Data collection. Indoor environments can be captured in many formats, including RGBD images, point clouds, and triangle meshes. Zhang et al. [29] uses panoramic RGBD images as input and reconstructs geometry using structure grammars, while [21] uses a 3D scan to extract plane primitives and generates models using heuristics. A single image is used in some deep learning methods [8,11,13,14,30] to generate cuboid-based layouts for a single room. Detailed semi-constrained floor plan computations for a complete house require processing a 3D scan of the house [15]; the complete scan increases accuracy, but increases computing requirements and time. Pintore and Gobbetti [23] proposed a technique to create floor plans and 3D models using an Android device camera, leveraging sensor data and statistical techniques. Chen et al. [7] introduced an augmented reality system utilizing the Microsoft Hololens for indoor layout assessment, addressing intuitive evaluation and efficiency challenges. In our approach, we begin with a triangle mesh from a HoloLens 2, using its Spatial Mapping software [18], which has been surveyed by Weinmann et al. [27]. Data collection. Floor plan computations Early methods [1,3,22,28] relied on image processing techniques, such as histograms and plane fitting, to create floor plans from 3D data. While [22] creates a floor plan by detecting vertical planes in a 3D point cloud, [3] uses planar structure extraction to create floor plans. These techniques rely on heuristics and were prone to failure due to noise in the data. Floor plan computations There has been much progress in floor plan computation using graphical models [4,9,12]. Such models [10] are also used to recover layouts and floor plans from crowd-sourced images and location data. One interactive tool [16] creates desirable floorplans by conforming to design constraints. Pintore et al. [24] characterizes several available input sources (including the triangle meshes that we use) and output models and discusses the main elements of the reconstruction pipeline. It also identifies several systems for producing floor plans, including FloorNet [15], and Floor-SP [6]. Monszpart et al. [19] introduced an algorithm that exploits the observation that distant walls are generally parallel to identify dominant wall directions using k-means. Our approach also utilizes k-means, but does so to identify walls in all directions, not just the dominant ones. Cai et al. [5] uses geometric priors, including point density, indoor area recognition, and normal information, to reconstruct floorplans from raw point clouds. In contrast to Arikan et al. [2], which employed a greedy algorithm to find plane normal directions and fit planes to points with help from user interaction, our approach is automatic. It also differs from the work of [20], which focuses on removing clutter and partitioning the interior into a 3D cell complex; our method specifically divides the building into separate walls. Our work is related to [22] and [26]. In [22], floor plan generation starts with a laser range data point cloud, followed by floor and ceiling detection using a height histogram. The remaining points are projected onto a ground plane, where a density histogram and Hough transform are applied to generate the line segments that form a floor plan. In projecting to 2D, their method risks losing information that may be useful for creating 3D models or detailed floor plans. Similarly, [26] utilizes a histogram-based approach to detect ceilings and floors. Their method involves identifying taller wall segments to create a 2D histogram, and then employing heuristics based on histogram point density to compute the floor plan. Our approach differs from [22] and [26] by aligning the mesh with global coordinate axes and not relying on laser data or a point cloud. Working primarily with 3D data throughout the pipeline, it benefits from enhanced information and generates both a 3D model and a floor plan. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Related Work

Authors:
(1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu;
(2) Eric Bier, Palo Alto Research Center, USA bier@parc.com;
(3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com;
(4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov;
(5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References Abstract This paper describes a novel approach for generating accurate floor plans and 3D models of building interiors using scanned mesh data. Unlike previous methods, which begin with a high resolution point cloud from a laser range-finder, our approach begins with triangle mesh data, as from a Microsoft HoloLens. It generates two types of floor plans, a "pen-and-ink" style that preserves details and a drafting-style that reduces clutter. It processes the 3D model for use in applications by aligning it with coordinate axes, annotating important objects, dividing it into stories, and removing the ceiling. Its performance is evaluated on commercial and residential buildings, with experiments to assess quality and dimensional accuracy. Our approach demonstrates promising potential for automatic digitization and orientation of scanned mesh data, enabling floor plan and 3D model generation in various applications such as navigation, interior design, furniture placement, facilities management, building construction, and HVAC design. Keywords: Clustering based methods · Floor plans · Augmented Reality· 3D Models. 1 Introduction Floor plans are useful for many applications including navigating in building interiors; remodeling; efficient placement of furniture; placement of pipes; heating, ventilation, and air conditioning (HVAC) design; and preparing an emergency evacuation plan. Depending on the application, different kinds of floor plan are appropriate. For remodeling building interiors or designing HVAC systems, users may prefer a drafting-style floor plan that focuses on planar walls and removes furniture and other clutter. For furniture placement, navigation, or evacuation planning, users may prefer a more detailed floor plan that shows the positions of furniture, cabinets, counter tops, etc. In either case, producing a floor plan can be time consuming, requiring expert skills, such as measuring distances and angles or entering data into a CAD program. Furthermore, it may need to be done more than once because a building changes when walls and furniture are moved, added, or removed. So it is valuable to be able to generate floor plans automatically with little or no training. To generate floor plans, it helps to begin with accurate data that can be collected automatically. Laser range finders, smartphones, tablets, and augmented reality (AR) headsets are some of the devices that have made it easier to collect high-resolution building data in the form of RGBD images, point clouds, and triangle meshes. In this paper, we describe a method for generating drafting style and pen-and-ink-style floor plans by leveraging incomplete and imperfect triangle mesh data. This approach efficiently generates both types of floor plans accurately, supporting a wide range of applications. Main Contribution We describe a new method for generating accurate floor plans using poorly captured triangle mesh data from the Microsoft HoloLens 2. The main contributions are: – A modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, using blocks to capture wall height and thickness. – An orientation-based clustering method that finds walls at arbitrary angles. – The use of k-means clustering to rotate the mesh to the principal axes and to identify the floor and ceiling. – Generating two kinds of precise floor plans from incomplete mesh data. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Authors: Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Abstract and Intro Related Work Related Work Methodology Methodology Experiments Experiments Conclusion & Future work and References Conclusion & Future work and References Abstract This paper describes a novel approach for generating accurate floor plans and 3D models of building interiors using scanned mesh data. Unlike previous methods, which begin with a high resolution point cloud from a laser range-finder, our approach begins with triangle mesh data, as from a Microsoft HoloLens. It generates two types of floor plans, a "pen-and-ink" style that preserves details and a drafting-style that reduces clutter. It processes the 3D model for use in applications by aligning it with coordinate axes, annotating important objects, dividing it into stories, and removing the ceiling. Its performance is evaluated on commercial and residential buildings, with experiments to assess quality and dimensional accuracy. Our approach demonstrates promising potential for automatic digitization and orientation of scanned mesh data, enabling floor plan and 3D model generation in various applications such as navigation, interior design, furniture placement, facilities management, building construction, and HVAC design. Keywords : Clustering based methods · Floor plans · Augmented Reality· 3D Models. Keywords 1 Introduction Floor plans are useful for many applications including navigating in building interiors; remodeling; efficient placement of furniture; placement of pipes; heating, ventilation, and air conditioning (HVAC) design; and preparing an emergency evacuation plan. Depending on the application, different kinds of floor plan are appropriate. For remodeling building interiors or designing HVAC systems, users may prefer a drafting-style floor plan that focuses on planar walls and removes furniture and other clutter. For furniture placement, navigation, or evacuation planning, users may prefer a more detailed floor plan that shows the positions of furniture, cabinets, counter tops, etc. In either case, producing a floor plan can be time consuming, requiring expert skills, such as measuring distances and angles or entering data into a CAD program. Furthermore, it may need to be done more than once because a building changes when walls and furniture are moved, added, or removed. So it is valuable to be able to generate floor plans automatically with little or no training. To generate floor plans, it helps to begin with accurate data that can be collected automatically. Laser range finders, smartphones, tablets, and augmented reality (AR) headsets are some of the devices that have made it easier to collect high-resolution building data in the form of RGBD images, point clouds, and triangle meshes. In this paper, we describe a method for generating drafting style and pen-and-ink-style floor plans by leveraging incomplete and imperfect triangle mesh data. This approach efficiently generates both types of floor plans accurately, supporting a wide range of applications. Main Contribution We describe a new method for generating accurate floor plans using poorly captured triangle mesh data from the Microsoft HoloLens 2. The main contributions are: Main Contribution – A modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, using blocks to capture wall height and thickness. – An orientation-based clustering method that finds walls at arbitrary angles. – The use of k-means clustering to rotate the mesh to the principal axes and to identify the floor and ceiling. – Generating two kinds of precise floor plans from incomplete mesh data. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Abstract and Introduction

is a core component for all ambitious businesses, but can loyalty be digitized to great effect? Customer retention Many businesses mull over how to optimize customer retention, but a startling number of decision-makers still struggle to understand that customer loyalty is about more than just selling products. Today, to retain more customers, businesses must apply their efforts and resources to provide a level of service that can appeal to customers in an engaging way. While it can be easy to lose the trust of customers, it takes time and care to build it up. Fortunately, these processes can be optimized in a digital environment to help build strong . relationships between a brand and customers Optimizing Social Engagement When it comes to leveraging a customer retention strategy, social media is a key consideration for virtually all businesses. Through social platforms, it’s possible to keep customers informed and nurture new leads about your brand, upcoming events, special offers, and product discounts. Crucially, are also excellent ways of attracting customers by demonstrating their credibility using other trustworthy sources. social proof and social selling Whether you use social media to highlight positive expert comments or an influencer endorsement, you can leverage the right platforms to showcase your brand and products in the best possible light. \In the above excerpt from the Starbucks X account, we can see that the brand is no stranger to utilizing social proof in the form of a positive repost or a celebrity endorsement to boost engagement. Build Analytical Insights from Behavior Customer loyalty offers key tangible and intangible benefits for businesses, particularly when it comes to data collection. Loyal customers who regularly interact with a business can indirectly offer more data and insight into predictive analysis initiatives. The great thing about the insights leveraged through is that they can provide actionable information on what exactly caused them to become so engaged with your business. customer loyalty Factors like your products with the best competitive advantage, the best target demographics for certain products, whether customers are willing to increase their spending, and the average can all be analyzed through the behavioral insights from your most loyal customers. lifetime value of a customer Create Rewards to Compliment Behavioral Patterns For rewards to be most effective, they must leverage a positive action. For instance, if you want to increase your average order value, a restaurant can add a complimentary drink with a certain meal deal. But how can you set up rewards to complement a ? There are three key principles that all businesses can follow to maximize their performance. digital customer retention strategy Firstly, businesses should go beyond discounts in order to reach new leads. Applying too many discounts can lead to negative perceptions of a product’s quality and the cheapness of your brand. Rather than discounting, adding bonuses like free delivery on orders over $50 can motivate customers to receive a reward for buying more. You should also look to distribute simple rewards for loyal customers. Rather than offering a freebie every 10 orders, keep customers motivated to come back by offering a smaller reward for every two or three orders. Finally, make sure that you are seen to recognize your customers through your rewards systems. Whether you offer them a special birthday discount or a reward as part of a referral scheme, this can help customers feel valued and motivated to continue promoting your brand. Leverage Personalization The single best way to generate more customer retention is to build an emotional connection between your customers and your brand. This can be done through personalization, and customers can feel more valued when their experiences are tailored to suit their own wants and needs. Statistics suggest that are more likely to make a purchase with personalized experiences, but leveraging personalization means utilizing more customer data. 80% of shoppers This is where the behavioral analytics you’ve amassed can become useful for developing insights based on previous purchases, brand preferences, and key shopping habits. Using this data in an affirmative way can help you showcase your most effective products and services under the noses of your most receptive leads. Building Segments for Lost Customers Another key component of customer retention is to reclaim your customers who have fallen through the cracks of your loyalty scheme in recent months. Be sure to set up a bespoke promotion that can lure lapsed customers back into making regular purchases with your business. In addition to this, customers who have abandoned an online shopping cart could that their items are still available to buy. receive email reminders These measures can actively encourage retention and show customers that your brand considers them valuable enough to engage with if something seems wrong. Harnessing the Power of Loyalty Managing customer retention is key for ambitious businesses of all sizes. It can be a powerful tool that positively impacts loyalty and maximizes growth prospects. By prioritizing customer needs and meeting them, a digitized strategy can help organizations build strong and lasting relationships with their customers. Leveraging loyalty is about more than just marketing a brand accordingly. It has to be a business mindset where the customer can feel prioritized through meaningful connections and interactions. It’s through this carefully structured approach that a business can build a sustainable connection with those who matter most.

Leveraging Loyalty: Can Businesses Digitize Customer Retention?

People around the world have been making them move towards increasingly digital lifestyles over the past few years – a trend that has been fast-tracked by the pandemic. More countries are swiftly adopting digitalization, even those that have previously been less inclined to make the shift. The global telecommunications industry has witnessed a drastic change in the way customers interact, demanding . Plus, there’s been an unprecedented increase in demand for digital services. In this scenario, telcos need to modernize their network infrastructure to remain competitive and relevant. The next revolutionary step in network transformation: zero-touch networks. digital-first customer experience (CX) What is the zero-touch network strategy? All-digital or enable operators to take all customer interactions online, through a website or mobile app. It digitizes the entire customer lifecycle: zero-touch networks subscribers self-register, select from a list of personalized services and plans, upload their identification documents for verification through eKYC, make payment, have the SIM delivered to their chosen postal address, and activate their account online. In fact, some operators are even eliminating the need for a physical SIM by implementing eSIM technology. The entire process is conveniently completed in a fraction of the time taken for traditional network offerings. Digital onboarding: dynamic, personalized, and intelligent customer journeys can be made available on various platforms, such as chat apps, social media platforms, community forums, and more. Operators get a 360-degree view of the customer, enabling them to provide contextual recommendations. The journeys are further enhanced with guided searches, continuity across platforms, and more. As more international and regional compliances come into effect, operators are provided a full security framework to tackle threats like identity fraud. Automated and secured customer journeys: since all touchpoints are digital, operators can gather and analyze data at every touchpoint. Advanced analytics and custom reports help operators understand the individual and preferences of different customer segments, enabling them to gauge and meet customer expectations. Advanced BI insights: Key benefits of zero-touch networks Automate processes Operators can automate CX by enabling chatbots, community forums, and other self-help tools; implement end-to-end process automation; and enable advanced upsell and cross-sell opportunities. Eliminate the need for physical touchpoints Zero-touch networks let customers access all sales and support services from any connected device. They can explore and purchase new plans and services, get instant support to resolve their issues through AI chatbot integrations that are available 24x7x365, and more. And it’s not just frontend processes; zero-touch networks also implement automation in the backend. The network infrastructure runs in the cloud, meaning operators no longer need to rely on physical resources. Grant customers full control over their accounts Zero-touch networks cater to customers who prefer to manage their own accounts and services, limiting their interaction with human agents. Subscribers can control their accounts through the service provider’s or website. They can also interact with the service provider through various social media platforms with the help of automated chatbot integrations. Using the platform/s of their choosing, they can build their own plans, set contract terms, choose what notifications they receive and when, and more. The chatbot integrations escalate issues to human agents only when they cannot be resolved by automated responses. self-care app Improve Customer Engagement Operators can improve the quality of customer engagement through more meaningful interactions. They can enrich customer value management with contextual and personalized campaigns, promotions, and loyalty programs. Ensure Faster Lead Conversion Operators can optimize lead generation and management, attracting younger and more digitally savvy subscribers, onboarding them and activating their accounts without any physical touchpoints or customer-facing agents. Stay Ahead of Competitors Operators who implement zero-touch networks today can compete with other telcos and OTT players. They can accelerate sales and support activities, leveraging advanced data insights to swiftly introduce relevant new services to meet evolving customer needs. This data can also help them create personalized and contextual new services for customers. Drive Revenue While Lowering Costs With a small physical resource footprint, operators can launch an all-digital brand with low initial investment and continue to keep operational costs low. The network operates in the cloud, reducing the need to manage elaborate IT infrastructure, in-bound call centers, and stores. Since all services and operations are digital, they can swiftly test, modify, launch, and monetize new services. Leverage Data Operators can improve customer segmentation and run marketing campaigns targeting specific segments. Learn how zero-touch networks can help you enable digital-first CX: Watch Now

The Revolutionary New Way to Digitize Customer Experience: Zero-touch Networks

Last summer, during my 14-day quarantine after flying home, my friend called me and told me her crazy idea. We talked about ways people can donate to those who were heavily impacted by COVID-19. We found out that unless we donated as an institution, it was actually difficult to do charity because we needed to ensure a few things: That the individual/organization doing charity is reliable That our donation comes to those who actually need it That the donation recipients receive just enough Most of the time, people don’t have time or may not be mindful enough to do careful research and follow-up with their donations. We needed a faster, more efficient, and transparent way to bridge the gap between those who can share and those in need. was born out of quarantine to remove all barriers to the donation process and empower community-giving. We ran in 2 months with operating in of Vietnam and delivered of rice to underprivileged students, workers, elders, and patients across Vietnam. App Gạo 2 beta tests 4 charity organizations 4 different provinces 12,818lbs (5814kg) Our future vision for App Gao was not limited to only rice. We wanted people to be able to donate a variety of products and services. We aimed for App Gao to become a management tool/network for all charity organizations to manage their activities/donations, including scheduling volunteering events that anyone can check and sign up to participate in (similar to how ClassPass works). Our team wanted App Gao to be the first thing that would come up in people’s minds when they think of charity. Although I didn’t understand what my developers talked about to one another at first, I unconsciously spent late nights scrolling through their messages to look up technical concepts and ask a team member to elaborate. I always thought that changes could only be made through policies, but App Gao proved me otherwise. March 2020 - May 2020: We found our talented team members working in 2 time zones - Vietnam and United States: Vicky Giap, Tuan Dao, Quang Khanh, Trang Dang Built our first business plan, which included a 5-year vision plan (Vicky and I are very compatible when it comes to executing crazy ideas) Met and partnered with Mr. Tran Dang Tuan - the person behind Vietnam’s most successful charity organization Talked to so many people about our idea, including Mr. Giap Van Duong - who suggested the name “App Gao”, and Mr. Ban Thai Hung - one of the biggest supporter of my crazy ideas of all time Launched our web-app version Launched our app version Created our Facebook page June 2020 - August 2020: Ran 2 beta tests: Delivered 670kg of rice to Xóm Chạy Thận Delivered 420kg of rice to Sa Lông Primary School in Mường Chà, Điện Biên through Quỹ Trò Nghèo Vùng Cao (Cơm Có Thịt) Delivered 550kg of rice to Chùa An Quốc Went on our first research trip to Mường Chà, Điện Biên to learn about the living conditions of people in mountainous areas (Watch my video ) here Had a news article written about us Deployed our first user survey, identifying 3 technical glitches Received a funding offer worth of 20,000 USD from Vietnam Silicon Valley (but we didn’t know anything about investing!) August 2020 - October 2020: Delivered 500kg of rice to Quỹ Trò Nghèo Vùng Cao (Cơm Có Thịt) Delivered 1852kg of rice to Quỹ Trò Nghèo Vùng Cao (Cơm Có Thịt) Delivered 1079kg of rice to Mỗi Ngày Một Quả Trứng (One Egg A Day): 800kg delivered to 40 families in Cư San, M'Drak 270kg delivered to families in Ea So, Ea Kar Looking back, there are a few ideas I wish we could have done or continued to implement/improve: App engagement and user retention features that would notify or remind people to make a donation weekly/monthly/annually. A charity map (which now exists in Vietnam!) that shows all places in need of help as red dots, and can turn into green once they received support from a user. Our Excel file is a mess. It’s important to keep data organized as that’s the only thing I’m looking at right now as I’m writing this to ensure I got the details right. We found a business model that worked. After looking into crowdfunding platforms like GoFundMe, we learned that at least 1% of users’ donations go to these platforms. However, as a charity app, it wouldn’t look good if we were to charge users money so they can make donations. We realized that we were technically a third-party seller for our rice suppliers, so we decided to charge them 1% for each purchase made by users. We tested this out for a month, and we got: !!! Although it wasn’t a lot of money, this number proved that our business model could work! We donated all of this money to the 2 charity organizations that partnered with us and guided us as we learned about the charity scene in Vietnam: Quỹ Trò Nghèo Vùng Cao (Cơm Có Thịt) and Mỗi Ngày Một Quả Trứng (One Egg A Day). 16,957,000VND (745 USD) We talked to so many people with expertise in law and finance to ask for their advice on how to give App Gao a legal entity (Non-profit? Or social enterprise? Who would represent us? Did we even have to pay tax? How much tax would we have to pay?). Since there are few social enterprises in Vietnam, and our business model was kind of new for an app, we didn’t find much information. We wanted to look for ways to grow App Gao, but who would be the CEO? COO? CTO? What would my team members’ roles be when we were all calling ourselves co-founders? It was clear that our team was small, and we needed to continue going on our own journeys so that one day, when we are equipped with the necessary knowledge and experience, we could actually make hard and important decisions with confidence. Although we are no longer working on App Gao, I want to document our big milestones. I think we were very successful for an idea that we didn’t think would receive so much support and attention. App Gao was a crucial life event that marked my growth both as a friend and a team player.

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