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GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs (2405.06782v1)
Published 10 May 2024 in cs.CV
Abstract: Accurate and effective 3D object detection is critical for ensuring the driving safety of autonomous vehicles. Recently, state-of-the-art two-stage 3D object detectors have exhibited promising performance. However, these methods refine proposals individually, ignoring the rich contextual information in the object relationships between the neighbor proposals. In this study, we introduce an object relation module, consisting of a graph generator and a graph neural network (GNN), to learn the spatial information from certain patterns to improve 3D object detection. Specifically, we create an inter-object relationship graph based on proposals in a frame via the graph generator to connect each proposal with its neighbor proposals. Afterward, the GNN module extracts edge features from the generated graph and iteratively refines proposal features with the captured edge features. Ultimately, we leverage the refined features as input to the detection head to obtain detection results. Our approach improves upon the baseline PV-RCNN on the KITTI validation set for the car class across easy, moderate, and hard difficulty levels by 0.82%, 0.74%, and 0.58%, respectively. Additionally, our method outperforms the baseline by more than 1% under the moderate and hard levels BEV AP on the test server.
- X. Zhou, M. Liu, B. L. Zagar, E. Yurtsever, and A. C. Knoll, “Vision language models in autonomous driving and intelligent transportation systems,” arXiv preprint arXiv:2310.14414, 2023.
- A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 12697–12705, 2019.
- 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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10529–10538, 2020.
- S. Shi, Z. Wang, J. Shi, X. Wang, and H. Li, “From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 8, pp. 2647–2664, 2021.
- E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. K. Caylı, and A. Knoll, “3d object detection with a self-supervised lidar scene flow backbone,” in European Conference on Computer Vision, pp. 247–265, Springer, 2022.
- H. Liang, C. Jiang, D. Feng, X. Chen, H. Xu, X. Liang, W. Zhang, Z. Li, and L. Van Gool, “Exploring geometry-aware contrast and clustering harmonization for self-supervised 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3293–3302, 2021.
- H. Wu, C. Wen, S. Shi, X. Li, and C. Wang, “Virtual sparse convolution for multimodal 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21653–21662, 2023.
- Y. Wang and J. M. Solomon, “Object dgcnn: 3d object detection using dynamic graphs,” Advances in Neural Information Processing Systems, vol. 34, pp. 20745–20758, 2021.
- R. Qian, X. Lai, and X. Li, “Badet: Boundary-aware 3d object detection from point clouds,” Pattern Recognition, vol. 125, p. 108524, 2022.
- D. Schinagl, G. Krispel, C. Fruhwirth-Reisinger, H. Possegger, and H. Bischof, “Gace: Geometry aware confidence enhancement for black-box 3d object detectors on lidar-data,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6566–6576, 2023.
- Y.-H. Wu, D. Zhang, L. Zhang, X. Zhan, D. Dai, Y. Liu, and M.-M. Cheng, “Ret3d: Rethinking object relations for efficient 3d object detection in driving scenes,” arXiv preprint arXiv:2208.08621, 2022.
- F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE transactions on neural networks, vol. 20, no. 1, pp. 61–80, 2008.
- W. Shi and R. Rajkumar, “Point-gnn: Graph neural network for 3d object detection in a point cloud,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1711–1719, 2020.
- Y. Bai, B. Fei, Y. Liu, T. Ma, Y. Hou, B. Shi, and Y. Li, “Rangeperception: Taming lidar range view for efficient and accurate 3d object detection,” Advances in Neural Information Processing Systems, vol. 36, 2024.
- J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander, “Joint 3d proposal generation and object detection from view aggregation,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1–8, IEEE, 2018.
- C. He, R. Li, Y. Zhang, S. Li, and L. Zhang, “Msf: Motion-guided sequential fusion for efficient 3d object detection from point cloud sequences,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5196–5205, 2023.
- J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun, “Graph neural networks: A review of methods and applications,” AI open, vol. 1, pp. 57–81, 2020.
- Y. Bi, A. Chadha, A. Abbas, E. Bourtsoulatze, and Y. Andreopoulos, “Graph-based object classification for neuromorphic vision sensing,” in Proceedings of the IEEE/CVF international conference on computer vision, pp. 491–501, 2019.
- Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph cnn for learning on point clouds,” ACM Transactions on Graphics (tog), vol. 38, no. 5, pp. 1–12, 2019.
- Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE transactions on neural networks and learning systems, vol. 32, no. 1, pp. 4–24, 2020.
- A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in 2012 IEEE conference on computer vision and pattern recognition, pp. 3354–3361, IEEE, 2012.
- M. Liu, E. Yurtsever, J. Fossaert, X. Zhou, W. Zimmer, Y. Cui, B. L. Zagar, and A. C. Knoll, “A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook,” IEEE Transactions on Intelligent Vehicles, pp. 1–29, 2024.
- Y. Yan, Y. Mao, and B. Li, “Second: Sparsely embedded convolutional detection,” Sensors, vol. 18, no. 10, p. 3337, 2018.
- W. Zheng, W. Tang, S. Chen, L. Jiang, and C.-W. Fu, “Cia-ssd: Confident iou-aware single-stage object detector from point cloud,” in Proceedings of the AAAI conference on artificial intelligence, vol. 35, pp. 3555–3562, 2021.
- S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” 2019.
- Y. Chen, S. Liu, X. Shen, and J. Jia, “Fast point r-cnn,” in Proceedings of the IEEE/CVF international conference on computer vision, pp. 9775–9784, 2019.
- L. Fan, X. Xiong, F. Wang, N. Wang, and Z. Zhang, “Rangedet: In defense of range view for lidar-based 3d object detection,” in Proceedings of the IEEE/CVF international conference on computer vision, pp. 2918–2927, 2021.
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