Real-Time Anchor-Free Single-Stage 3D Detection with IoU-Awareness
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Abstract (arXiv)
In this report, we introduce our winning solution to the Real-time 3D Detection and also the "Most Efficient Model" in the Waymo Open Dataset Challenges at CVPR 2021. Extended from our last year's award-winning model AFDet, we have made a handful of modifications to the base model, to improve the accuracy and at the same time to greatly reduce the latency. The modified model, named as AFDetV2, is featured with a lite 3D Feature Extractor, an improved RPN with extended receptive field and an added sub-head that produces an IoU-aware confidence score. These model enhancements, together with enriched data augmentation, stochastic weights averaging, and a GPU-based implementation of voxelization, lead to a winning accuracy of 73.12 mAPH/L2 for our AFDetV2 with a latency of 60.06 ms, and an accuracy of 72.57 mAPH/L2 for our AFDetV2-base, entitled as the "Most Efficient Model" by the challenge sponsor, with a winning latency of 55.86 ms.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 29 references that are in the map. First posted on arXiv 29 Jul 2021.
Builds on
22 of 29 references.
References · 22
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10 more
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Built on by
Citing papers per year: 2021: 1; 2022: 3.
4 papers.
- 2021AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Cloudscited by 88
- 2022Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completioncited by 82
- 2022CG-SSD: Corner Guided Single Stage 3D Object Detection from LiDAR Point Cloudcited by 5
- 2022MT-Net Submission to the Waymo 3D Detection Leaderboardcited by 5
- 2022MT-Net Submission to the Waymo 3D Detection Leaderboard11 Jul 2022
- 2022Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion18 Mar 2022
- 2022CG-SSD: Corner Guided Single Stage 3D Object Detection from LiDAR Point Cloud24 Feb 2022
- 2021AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds16 Dec 2021