Papers › Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
Jiaming Sun, Linghao Chen, Yiming Xie, Siyu Zhang, Qinhong Jiang, Xiaowei Zhou, Hujun Bao
In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images. Many recent works solve this problem by first recovering a point cloud with disparity estimation and then apply a 3D detector. The disparity map is computed for the entire image, which is costly and fails to leverage category-specific prior. In contrast, we design an instance disparity estimation network (iDispNet) that predicts disparity only for pixels on objects of interest and learns a category-specific shape prior for more accurate disparity estimation. To address the challenge from scarcity of disparity annotation in training, we propose to use a statistical shape model to generate dense disparity pseudo-ground-truth without the need of LiDAR point clouds, which makes our system more widely applicable. Experiments on the KITTI dataset show that, even when LiDAR ground-truth is not available at training time, Disp R-CNN achieves competitive performance and outperforms previous state-of-the-art methods by 20% in terms of average precision.
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Tasks
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 | Disp R-CNN | AP75 | 45.78 | #6 of 12 | Archive leaderboard | report |
| 3D Object Detection From Stereo Images | KITTI Cyclists Moderate | Disp R-CNN | AP50 | 24.40 | #3 of 5 | Archive leaderboard | report |
| 3D Object Detection From Stereo Images | KITTI Pedestrians Moderate | Disp R-CNN | AP50 | 25.80 | #3 of 6 | Archive leaderboard | report |
| Vehicle Pose Estimation | KITTI Cars Hard | Disp-RCNN (Stereo) | Average Orientation Similarity | 67.16 | #14 of 19 | 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: Disp R-CNN
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