Papers › DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors
DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors
Yilun Chen, Shijia Huang, Shu Liu, Bei Yu, Jiaya Jia
Camera-based 3D object detectors are welcome due to their wider deployment and lower price than LiDAR sensors. We first revisit the prior stereo detector DSGN for its stereo volume construction ways for representing both 3D geometry and semantics. We polish the stereo modeling and propose the advanced version, DSGN++, aiming to enhance effective information flow throughout the 2D-to-3D pipeline in three main aspects. First, to effectively lift the 2D information to stereo volume, we propose depth-wise plane sweeping (DPS) that allows denser connections and extracts depth-guided features. Second, for grasping differently spaced features, we present a novel stereo volume -- Dual-view Stereo Volume (DSV) that integrates front-view and top-view features and reconstructs sub-voxel depth in the camera frustum. Third, as the foreground region becomes less dominant in 3D space, we propose a multi-modal data editing strategy -- Stereo-LiDAR Copy-Paste, which ensures cross-modal alignment and improves data efficiency. Without bells and whistles, extensive experiments in various modality setups on the popular KITTI benchmark show that our method consistently outperforms other camera-based 3D detectors for all categories. Code is available at https://github.com/chenyilun95/DSGN2.
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Code
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Detection From Stereo Images | KITTI Cars Moderate | DSGN++ | AP75 | 67.37 | #1 of 12 | Archive leaderboard | report |
| 3D Object Detection From Stereo Images | KITTI Cyclists Moderate | DSGN++ | AP50 | 43.90 | #1 of 5 | Archive leaderboard | report |
| 3D Object Detection From Stereo Images | KITTI Pedestrians Moderate | DSGN++ | AP50 | 32.74 | #1 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: Depth-wise Plane Sweeping
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