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Recent advancements in bird's eye view (BEV) representations have shown remarkable promise for in-vehicle 3D perception.
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Y. Zhou and O. Tuzel, “VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2018, pp. 4490–4499
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Y. Yan, Y. Mao, and B. Li, “SECOND: Sparsely Embedded Convolutional Detection,” Sensors , vol. 18, no. 10, p. 3337, 2018
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Y. Yan, Y. Mao, and B. Li, “SECOND: Sparsely Embedded Convolutional Detection,” Sensors , 2018, vol. 18, pp. 3337
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Y. Zhou and O. Tuze, “VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2018, pp. 4490–4499
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B. Wu, A. Wan, X. Yue, and K. Keutzer, “SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud,” in IEEE Int. Conf. Robot. Autom. , 2018, pp. 1887–1893
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A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “PointPillars: Fast Encoders for Object Detection from Point Clouds,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2019, pp. 12 697–12 705
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A. Barbu, D. Mayo, J. Alverio, W. Luo, C. Wang, D. Gutfreund, J. Tenenbaum, and B. Katz, “ObjectNet: A Large-Scale Bias-Controlled Dataset for Pushing the Limits of Object Recognition Models,” in Adv. Neural Inf. Process. Syst. , vol. 32, 2019
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S. Shi, X. Wang, and H. Li, “PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2019, pp. 770–779
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H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. Guibas, “KPConv: Flexible and Deformable Convolution for Point Clouds,” in IEEE/CVF Int. Conf. Comput. Vis. , 2019, pp. 6411–6420
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A. Milioto, I. Vizzo, J. Behley, and C. Stachniss, “RangeNet++: Fast and Accurate LiDAR Semantic Segmentation,” in IEEE/RSJ Int. Conf. Intell. Robots Syst. , 2019, pp. 4213–4220
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H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuScenes: A Multimodal Dataset for Autonomous Driving,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2020, pp. 11 621–11 631
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J. Tu, M. Ren, S. Manivasagam, M. Liang, B. Yang, R. Du, F. Cheng, and R. Urtasun, “Physically Realizable Adversarial Examples for LiDAR Object Detection,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2020, pp. 13 716–13 725
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S. Vora, A. H. Lang, B. Helou, and O. Beijbom, “PointPainting: Sequential Fusion for 3D Object Detection,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2020, pp. 4604–4612
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M. Contributors, “MMDetection3D: OpenMMLab Next-Generation Platform for General 3D Object Detection,” https://github.com/open-mmlab/mmdetection3d , 2020
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Y. Lee and J. Park, “CenterMask: Real-Time Anchor-Free Instance Segmentation,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2020, pp. 13 906–13 915
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V. Guizilini, R. Ambrus, S. Pillai, A. Raventos, and A. Gaidon, “3D Packing for Self-Supervised Monocular Depth Estimation,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2020, pp. 2485–2494
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W. Shi and R. Raj, “Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2020, pp. 1711–1719
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Z. Yang, Y. Sun, S. Liu, and J. Jia, “3DSSD: Point-Based 3D Single Stage Object Detector,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2020, pp. 11 040–11 048
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S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang, and H. Li, “PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2020, pp. 10 529–10 583
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Y. Zhang, Z. Zhou, P. David, X. Yue, Z. Xi, B. Gong, and H. Foroosh, “PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2020, pp. 9601-9610
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2020
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H. Tang, Z. Liu, S. Zhao, Y. Lin, J. Lin, H. Wang, and S. Han, “Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution,” in Eur. Conf. Comput. Vis. , 2020, pp. 685–702
2020
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2021
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T. Wang, X. Zhu, J. Pang, and D. Lin, “FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection,” in IEEE/CVF Int. Conf. Comput. Vis. , 2021, pp. 913–922
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M. Wortsman, G. Ilharco, J. W. Kim, M. Li, S. Kornblith, R. Roelofs, R. G. Lopes, H. Hajishirzi, A. Farhadi, H. Namkoong et al. , “Robust Fine-Tuning of Zero-Shot Models,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2022, pp. 7959–7971
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A. Fang, G. Ilharco, M. Wortsman, Y. Wan, V. Shankar, A. Dave, and L. Schmidt, “Data Determines Distributional Robustness in Contrastive Language Image Pre-Training (CLIP),” in Int. Conf. Mach. Learn. , 2022, pp. 6216–6234
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M. Wortsman, G. Ilharco, S. Y. Gadre, R. Roelofs, R. Gontijo-Lopes, A. S. Morcos, H. Namkoong, A. Farhadi, Y. Carmon, S. Kornblith et al. , “Model Soups: Averaging Weights of Multiple Fine-Tuned Models Improves Accuracy Without Increasing Inference Time,” in Int. Conf. Mach. Learn. , 2022, pp. 23 965–23 998
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M. Jaritz, T. H. Vu, R. Charette, E. Wirbel, and P. Pérez, “Cross-Modal Learning for Domain Adaptation in 3D Semantic Segmentation,” in IEEE Trans. Pattern Anal. Mach. Intell. , 2023, vol. 45, no. 2, pp. 1533–1544
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Y. Cao, N. Wang, C. Xiao, D. Yang, J. Fang, R. Yang, Q. A. Chen, M. Liu, and B. Li, “Invisible for Both Camera and LiDAR: Security of Multi-Sensor Fusion Based Perception in Autonomous Driving Under Physical-World Attacks,” in IEEE Symposium on Security and Privacy , 2021, pp. 176–194
2021
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Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows,” in IEEE/CVF Int. Conf. Comput. Vis. , 2021, pp. 10 012–10 022
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P. Sun, R. Zhang, Y. Jiang, T. Kong, C. Xu, W. Zhan, M. Tomizuka, L. Li, Z. Yuan, C. Wang et al. , “Sparse R-CNN: End-to-End Object Detection with Learnable Proposals,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2021, pp. 14 454–14 463
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D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, “Natural Adversarial Examples,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2021, pp. 15 262–15 271
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D. Hendrycks, S. Basart, N. Mu, S. Kadavath, F. Wang, E. Dorundo, R. Desai, T. Zhu, S. Parajuli, M. Guo et al. , “The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization,” in IEEE/CVF Int. Conf. Comput. Vis. , 2021, pp. 8340–8349
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T. Yin, X. Zhou, and P. Krahenbuhl, “Center-Based 3D Object Detection and Tracking,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2021, pp. 11 784–11 793
2021
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J. Xu, R. Zhang, J. Dou, Y. Zhu, J. Sun, and S. Pu, “RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation,” in IEEE/CVF Int. Conf. Comput. Vis. , 2021, pp. 16 024–16 033
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D. Park, R. Ambrus, V. Guizilini, J. Li, and A. Gaidon, “Is Pseudo-LiDAR Needed for Monocular 3D Object Detection?” in IEEE/CVF Int. Conf. Comput. Vis. , 2021, pp. 3142–3152
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2023
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K. Chitta, A. Prakash, B. Jaeger, Z. Yu, K. Renz, and A. Geiger, “TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving,” in IEEE Trans. Pattern Anal. Mach. Intell. , 2023, vol. 45, no. 11, pp. 12 878–12 895
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L. Kong, J. Ren, L. Pan, and Z. Liu, “LaserMix for Semi-Supervised LiDAR Semantic Segmentation,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2023, pp. 21 705–21 715
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R. Chen, Y. Liu, L. Kong, X. Zhu, Y. Ma, Y. Li, Y. Hou, Y. Qiao, and W. Wang, “Clip2Scene: Towards Label-Efficient 3D Scene Understanding by CLIP,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2023, pp. 7020–7030
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A. Ando, S. Gidaris, A. Bursuc, G. Puy, A. Boulch, and R. Marlet, “RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2023, pp. 5240–5250
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R. Chen, Y. Liu, L. Kong, N. Chen, X. Zhu, Y. Ma, T. Liu, and W. Wang, “Towards Label-Free Scene Understanding by Vision Foundation Models,” in Adv. Neural Inf. Process. Syst. , 2023
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Z. Liu, H. Tang, A. Amini, X. Yang, H. Mao, D. Rus, and S. Han, “BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation,” in IEEE Int. Conf. Robot. Autom. , 2023
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Z. Zhu, Y. Zhang, H. Chen, Y. Dong, S. Zhao, W. Ding, J. Zhong, and S. Zheng, “Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2023, pp. 21 600–21 610
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F. Bartoccioni, É. Zablocki, A. Bursuc, P. Pérez, M. Cord, and K. Alahari, “Lara: Latents and Rays for Multi-Camera Bird’s-Eye-View Semantic Segmentation,” in Conf. Robot Learn. PMLR, 2023, pp. 1663–1672
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L. Kong, Y. Liu, X. Li, R. Chen, W. Zhang, J. Ren, L. Pan, K. Chen, and Z. Liu, “Robo3D: Towards Robust and Reliable 3D Perception Against Corruptions,” in IEEE/CVF Int. Conf. Comput. Vis. , 2023, pp. 19 994–20 006
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C. Ge, J. Chen, E. Xie, Z. Wang, L. Hong, H. Lu, Z. Li, and P. Luo, “MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation,” in IEEE/CVF Int. Conf. Comput. Vis. , 2023, pp. 8721–8731
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X. Chen, T. Zhang, Y. Wang, Y. Wang, and H. Zhao, “FUTR3D: A Unified Sensor Fusion Framework for 3D Detection,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2023, pp. 172–181
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L. Kong, S. Xie, H. Hu, L. X. Ng, B. R. Cottereau, and W. T. Ooi, “RoboDepth: Robust Out-of-Distribution Depth Estimation Under Corruptions,” in Adv. Neural Inf. Process. Syst. , 2023
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L. Kong, Y. Liu, R. Chen, Y. Ma, X. Zhu, Y. Li, Y. Hou, Y. Qiao and Z. Liu, “Rethinking Range View Representation for LiDAR Segmentation,” in IEEE/CVF Int. Conf. Comput. Vis. , 2023, pp. 228–240
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L. Kong, N. Quader, and V. E. Liong, “ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation,” in IEEE Int. Conf. Robot. Autom. , 2023, pp. 9338–9345
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S. Goyal, A. Kumar, S. Garg, Z. Kolter, and A. Raghunathan, “Finetune Like You Pretrain: Improved Finetuning of Zero-Shot Vision Models,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2023, pp. 19 338–19 347
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H. Li, C. Sima, J. Dai, W. Wang, L. Lu, H. Wang, J. Zeng, Z. Li, J. Yang, H. Deng, H. Tian, E. Xie, J. Xie, L. Chen, T. Li, Y. Li, Y. Gao, X. Jia, S. Liu, J. Shi, D. Lin, and Y. Qiao, “Delving into the Devils of Bird’s-Eye-View Perception: A Review, Evaluation and Recipe,” in IEEE Trans. Pattern Anal. Mach. Intell. , 2024, vol. 46, no. 4, pp. 2151–2170
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Y. Liu, L. Kong, X. Wu, R. Chen, X. Li, L. Pan, Z. Liu, and Y. Ma, “Multi-Space Alignments Toward Universal LiDAR Segmentation,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2024
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F. Hong, L. Kong, H. Zhou, X. Zhu, H. Li, and Z. Liu, “Unified 3D and 4D Panoptic Segmentation via Dynamic Shifting Networks,” in IEEE Trans. Pattern Anal. Mach. Intell. , 2024, vol. 46, no. 5, pp. 3480–3495
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S. Chen, Y. Ma, Y. Qiao, and Y. Wang, “M-BEV: Masked BEV Perception for Robust Autonomous Driving,” in AAAI Conf. Artif. Intell. , vol. 38, no. 2, 2024, pp. 1183–1191
2024
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