Table of links ABSTRACT 1 INTRODUCTION 2 BACKGROUND: OMNIDIRECTIONAL 3D OBJECT DETECTION 3 PRELIMINARY EXPERIMENT 3.1 Experiment Setup 3.2 Observations 3.3 Summary and Challenges 4 OVERVIEW OF PANOPTICUS 5 MULTI-BRANCH OMNIDIRECTIONAL 3D OBJECT DETECTION 5.1 Model Design 6 SPATIAL-ADAPTIVE EXECUTION 6.1 Performance Prediction 5.2 Model Adaptation 6.2 Execution Scheduling 7 IMPLEMENTATION 8 EVALUATION 8.1 Testbed and Dataset 8.2 Experiment Setup 8.3 Performance 8.4 Robustness 8.5 Component Analysis 8.6 Overhead 9 RELATED WORK 10 DISCUSSION AND FUTURE WORK 11 CONCLUSION AND REFERENCES 7 IMPLEMENTATION We implemented Panopticus using Python and CUDA for GPU-based acceleration. All neural networks were developed using PyTorch [41] and trained on the training set in the nuScenes dataset [2]. Note that Panopticus is compatible with other 3D perception datasets such as Waymo [46], having sensor configurations similar to nuScenes. The neural networks for 3D object detection are developed based on Figure 9: Setup for mobile testbed and dataset. MMDetection3D [35]. For the camera motion network, we customized and trained [53] to produce consistent relative poses between two consecutive frames for any given camera view. For the object tracker, we modified SimpleTrack [36] to use velocity for 3D Kalman’s state transition model. We used the GPU-accelerated XGBoost library [12] and linear model in scikit-learn [43] for performance predictors, and PuLP [17] for the ILP solver. In the model adaptation stage, memory usage is profiled via tegrastats We optimized the performance of our multi-branch model in various ways. We used TensorRT [11] to modularize the neural networks and to accelerate the inference. Networks converted to TensorRT are optimized using floating-point 16 (FP16) quantization and layer fusion, etc. Camera view images assigned to the same networks, such as backbone network or DepthNet, are batch-processed together. Additionally, we used CUDA Multi-Stream [5] to process these multiple networks in parallel on an edge GPU. Meanwhile, we noticed an accuracy loss due to the simplistic modularization of our multi-branch model. Specifically, DepthNets and BEV head trained with a specific backbone network are incompatible with others. One-size-fits-all DepthNet is not practical since each backbone generates a 2D feature map of different sizes. Thus, our model includes DepthNet variants tailored to each backbone, ensuring accurate depth estimation. In contrast, the BEV head takes inputs of a consistent shape, regardless of the preceding networks—backbones and DepthNets. This allows us to train a universal BEV head compatible with any combination of preceding networks. We trained the BEV head from scratch, while the pre-trained backbones and DepthNets were fine-tuned. Authors:

Jeho Lee
Chanyoung Jung
Jiwon Kim
Hojung Cha This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. Table of links Table of links ABSTRACT ABSTRACT ABSTRACT 1 INTRODUCTION 1 INTRODUCTION 1 INTRODUCTION 2 BACKGROUND: OMNIDIRECTIONAL 3D OBJECT DETECTION 2 BACKGROUND: OMNIDIRECTIONAL 3D OBJECT DETECTION 2 BACKGROUND: OMNIDIRECTIONAL 3D OBJECT DETECTION 3 PRELIMINARY EXPERIMENT 3 PRELIMINARY EXPERIMENT 3 PRELIMINARY EXPERIMENT 3.1 Experiment Setup 3.2 Observations 3.3 Summary and Challenges 4 OVERVIEW OF PANOPTICUS 4 OVERVIEW OF PANOPTICUS 4 OVERVIEW OF PANOPTICUS 5 MULTI-BRANCH OMNIDIRECTIONAL 3D OBJECT DETECTION 5 MULTI-BRANCH OMNIDIRECTIONAL 3D OBJECT DETECTION 5 MULTI-BRANCH OMNIDIRECTIONAL 3D OBJECT DETECTION 5.1 Model Design 6 SPATIAL-ADAPTIVE EXECUTION 6 SPATIAL-ADAPTIVE EXECUTION 6 SPATIAL-ADAPTIVE EXECUTION 6.1 Performance Prediction 5.2 Model Adaptation 6.2 Execution Scheduling 7 IMPLEMENTATION 7 IMPLEMENTATION 7 IMPLEMENTATION 8 EVALUATION 8 EVALUATION 8 EVALUATION 8.1 Testbed and Dataset 8.2 Experiment Setup 8.3 Performance 8.4 Robustness 8.5 Component Analysis 8.6 Overhead 9 RELATED WORK 9 RELATED WORK 9 RELATED WORK 10 DISCUSSION AND FUTURE WORK 10 DISCUSSION AND FUTURE WORK 10 DISCUSSION AND FUTURE WORK 11 CONCLUSION AND REFERENCES 11 CONCLUSION AND REFERENCES 11 CONCLUSION AND REFERENCES 7 IMPLEMENTATION 7 IMPLEMENTATION We implemented Panopticus using Python and CUDA for GPU-based acceleration. All neural networks were developed using PyTorch [41] and trained on the training set in the nuScenes dataset [2]. Note that Panopticus is compatible with other 3D perception datasets such as Waymo [46], having sensor configurations similar to nuScenes. The neural networks for 3D object detection are developed based on Figure 9: Setup for mobile testbed and dataset. MMDetection3D [35]. For the camera motion network, we customized and trained [53] to produce consistent relative poses between two consecutive frames for any given camera view. For the object tracker, we modified SimpleTrack [36] to use velocity for 3D Kalman’s state transition model. We used the GPU-accelerated XGBoost library [12] and linear model in scikit-learn [43] for performance predictors, and PuLP [17] for the ILP solver. In the model adaptation stage, memory usage is profiled via tegrastats We optimized the performance of our multi-branch model in various ways. We used TensorRT [11] to modularize the neural networks and to accelerate the inference. Networks converted to TensorRT are optimized using floating-point 16 (FP16) quantization and layer fusion, etc. Camera view images assigned to the same networks, such as backbone network or DepthNet, are batch-processed together. Additionally, we used CUDA Multi-Stream [5] to process these multiple networks in parallel on an edge GPU. Meanwhile, we noticed an accuracy loss due to the simplistic modularization of our multi-branch model. Specifically, DepthNets and BEV head trained with a specific backbone network are incompatible with others. One-size-fits-all DepthNet is not practical since each backbone generates a 2D feature map of different sizes. Thus, our model includes DepthNet variants tailored to each backbone, ensuring accurate depth estimation. In contrast, the BEV head takes inputs of a consistent shape, regardless of the preceding networks—backbones and DepthNets. This allows us to train a universal BEV head compatible with any combination of preceding networks. We trained the BEV head from scratch, while the pre-trained backbones and DepthNets were fine-tuned. Authors: Jeho Lee
Chanyoung Jung
Jiwon Kim
Hojung Cha Authors: Jeho Lee Chanyoung Jung Jiwon Kim Hojung Cha Jeho Lee Jeho Lee Chanyoung Jung Chanyoung Jung Jiwon Kim Jiwon Kim Hojung Cha Hojung Cha This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

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