Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work


Method
3.1 Overview of Our Method
3.2 Coarse Text-cell Retrieval
3.3 Fine Position Estimation
3.4 Training Objectives


Experiments
4.1 Dataset Description and 4.2 Implementation Details
4.3 Evaluation Criteria and 4.4 Results


Performance Analysis
5.1 Ablation Study
5.2 Qualitative Analysis
5.3 Text Embedding Analysis


Conclusion and References Related Work Related Work Related Work Method
3.1 Overview of Our Method
3.2 Coarse Text-cell Retrieval
3.3 Fine Position Estimation
3.4 Training Objectives Method 3.1 Overview of Our Method 3.1 Overview of Our Method 3.2 Coarse Text-cell Retrieval 3.2 Coarse Text-cell Retrieval 3.3 Fine Position Estimation 3.3 Fine Position Estimation 3.4 Training Objectives 3.4 Training Objectives Experiments
4.1 Dataset Description and 4.2 Implementation Details
4.3 Evaluation Criteria and 4.4 Results Experiments 4.1 Dataset Description and 4.2 Implementation Details 4.1 Dataset Description and 4.2 Implementation Details 4.3 Evaluation Criteria and 4.4 Results 4.3 Evaluation Criteria and 4.4 Results Performance Analysis
5.1 Ablation Study
5.2 Qualitative Analysis
5.3 Text Embedding Analysis Performance Analysis 5.1 Ablation Study 5.1 Ablation Study 5.2 Qualitative Analysis 5.2 Qualitative Analysis 5.3 Text Embedding Analysis 5.3 Text Embedding Analysis Conclusion and References Conclusion and References Conclusion and References Supplementary Material Supplementary Material Details of KITTI360Pose Dataset
More Experiments on the Instance Query Extractor
Text-Cell Embedding Space Analysis
More Visualization Results
Point Cloud Robustness Analysis Details of KITTI360Pose Dataset Details of KITTI360Pose Dataset More Experiments on the Instance Query Extractor More Experiments on the Instance Query Extractor Text-Cell Embedding Space Analysis Text-Cell Embedding Space Analysis More Visualization Results More Visualization Results Point Cloud Robustness Analysis Point Cloud Robustness Analysis Anonymous Authors Anonymous Authors Details of KITTI360Pose Dataset
More Experiments on the Instance Query Extractor
Text-Cell Embedding Space Analysis
More Visualization Results
Point Cloud Robustness Analysis Details of KITTI360Pose Dataset Details of KITTI360Pose Dataset More Experiments on the Instance Query Extractor More Experiments on the Instance Query Extractor Text-Cell Embedding Space Analysis Text-Cell Embedding Space Analysis More Visualization Results More Visualization Results Point Cloud Robustness Analysis Point Cloud Robustness Analysis 4 EXPERIMENTS 4.1 Dataset Description We evaluate our IFRP-T2P model on the KITTI360Pose dataset [21]. This dataset encompasses 3D point cloud scenes from nine urban areas, spanning a city-scale space of 15.51 square kilometers and consisting of 43,381 paired descriptions and positions. We utilize five areas for our training set and one for validation. The remaining three areas are used for testing. Each 3D cell within this dataset is represented by a cube measuring 30 meters on each side, with a 10-meter stride between each cell. More details are provided in the supplementary material. 4.2 Implementation Details Our approach begins with pre-training the instance query extractor. This is accomplished through the instance segmentation objective, as outlined in Section 3.2. Our feature backbone employs a sparse convolution U-Net, specifically a Minkowski Res16UNet34C [10] with 0.15 meter voxel size. To align with the requirements of the KITTI360Pose configuration, we utilize cell point clouds, each encompassing a 30 meter cubic space, as our input. The training extends over 300 epochs, utilizing the AdamW optimizer. In the coarse stage, we train the text-cell retrieval model with AdamW optimizer with a learning rate of 1e-3. The model is trained for a total of 24 epochs while the learning rate is decayed by 10 at the 12-th epoch. In the fine stage, we train the regression model with Adam optimizer with a learning rate of 3e-4 for 12 epochs. Authors:
(1) Lichao Wang, FNii, CUHKSZ (wanglichao1999@outlook.com);
(2) Zhihao Yuan, FNii and SSE, CUHKSZ (zhihaoyuan@link.cuhk.edu.cn);
(3) Jinke Ren, FNii and SSE, CUHKSZ (jinkeren@cuhk.edu.cn);
(4) Shuguang Cui, SSE and FNii, CUHKSZ (shuguangcui@cuhk.edu.cn);
(5) Zhen Li, a Corresponding Author from SSE and FNii, CUHKSZ (lizhen@cuhk.edu.cn). Authors: Authors: (1) Lichao Wang, FNii, CUHKSZ (wanglichao1999@outlook.com); (2) Zhihao Yuan, FNii and SSE, CUHKSZ (zhihaoyuan@link.cuhk.edu.cn); (3) Jinke Ren, FNii and SSE, CUHKSZ (jinkeren@cuhk.edu.cn); (4) Shuguang Cui, SSE and FNii, CUHKSZ (shuguangcui@cuhk.edu.cn); (5) Zhen Li, a Corresponding Author from SSE and FNii, CUHKSZ (lizhen@cuhk.edu.cn). This paper is available on arxiv under CC BY-NC-ND 4.0 Deed (Attribution-Noncommercial-Noderivs 4.0 International) license. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed (Attribution-Noncommercial-Noderivs 4.0 International) license. available on arxiv available on arxiv

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