AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds
Yihan Hu, Zhuangzhuang Ding, Runzhou Ge, Wenxin Shao, Li Huang, Kun Li, Qiang Liu
摘要
There have been two streams in the 3D detection from point clouds: single-stage methods and two-stage methods. While the former is more computationally efficient, the latter usually provides better detection accuracy. By carefully examining the two-stage approaches, we have found that if appropriately designed, the first stage can produce accurate box regression. In this scenario, the second stage mainly rescores the boxes such that the boxes with better localization get selected. From this observation, we have devised a single-stage anchor-free network that can fulfill these requirements. This network, named AFDetV2, extends the previous work by incorporating a self-calibrated convolution block in the backbone, a keypoint auxiliary supervision, and an IoU prediction branch in the multi-task head. We take a simple product of the predicted IoU score with the classification heatmap to form the final classification confidence. The enhanced backbone strengthens the box localization capability, and the rescoring approach effectively joins the object presence confidence and the box regression accuracy. As a result, the detection accuracy is drastically boosted in the single-stage. To evaluate our approach, we have conducted extensive experiments on the Waymo Open Dataset and the nuScenes Dataset. We have observed that our AFDetV2 achieves the state-of-the-art results on these two datasets, superior to all the prior arts, including both the single-stage and the two-stage 3D detectors. AFDetV2 won the 1st place in the Real-Time 3D Detection of the Waymo Open Dataset Challenge 2021. In addition, a variant of our model AFDetV2-Base was entitled the "Most Efficient Model" by the Challenge Sponsor, showing a superior computational efficiency. To demonstrate the generality of this single-stage method, we have also applied it to the first stage of the two-stage networks. Without exception, the results show that with the strengthened backbone and the rescoring approach, the second stage refinement is no longer needed.
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引用它的顶会 Paper32
- Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object DetectionGuowen Zhang, Lue Fan, Chenhang He, Zhen Lei 等NeurIPS 2024 · 被引用 137 次
- FocalFormer3D : Focusing on Hard Instance for 3D Object DetectionYilun Chen, Zhiding Yu, Yukang Chen, Shiyi Lan 等ICCV 2023 · 被引用 109 次
- LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR PerceptionDongqiangzi Ye, Zixiang Zhou, Weijia Chen, Yufei Xie 等AAAI 2023 · 被引用 108 次
- HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point CloudsGang Zhang, Junnan Chen, Guohuan Gao, Jianmin Li 等NeurIPS 2023 · 被引用 95 次
- LION: Linear Group RNN for 3D Object Detection in Point CloudsZhe Liu, Jinghua Hou, Xinyu Wang, Xiaoqing Ye 等NeurIPS 2024 · 被引用 84 次
它引用的顶会 Paper21
- Deep Hough Voting for 3D Object Detection in Point CloudsCharles R. Qi, Or Litany, Kaiming He, Leonidas J. GuibasICCV 2019 · 被引用 1,467 次
- STD: Sparse-to-Dense 3D Object Detector for Point CloudZetong Yang, Yanan Sun, Shu Liu, Xiaoyong Shen 等ICCV 2019 · 被引用 840 次
- Improving 3D Object Detection with Channel-wise TransformerHualian Sheng, Sijia Cai, Yuan Liu, Bing Deng 等ICCV 2021 · 被引用 293 次
- RangeDet: In Defense of Range View for LiDAR-based 3D Object DetectionLue Fan, Xuan Xiong, Feng Wang, Naiyan Wang 等ICCV 2021 · 被引用 268 次
- Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical VoxelizationQi Chen, Lin Sun, Ernest Cheung, Alan L. YuilleNeurIPS 2020 · 被引用 124 次
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