Research map / Detection and segmentation / 3D detection and driving
Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use pinhole-geometry proposals followed by proposal-offset regression instead of direct depth estimation (ablation) | MonoPSR · KITTI val1, hard car | centroid depth error (average absolute error / standard deviation, m): 1.48 / 2.28 -> 1.22 / 1.84 | Helped |
| add full-image features to proposal regression (ablation) | MonoPSR proposal regression · KITTI val1, hard car | centroid depth error (average absolute error / standard deviation, m): 1.31 / 1.93 -> 1.22 / 1.84 | Helped |
| add local point cloud estimation (ablation) | MonoPSR proposal-regression baseline · KITTI val1, moderate car, 0.5 IoU | AP_3D: 34.47 -> 39.33 | Helped |
| add depth loss to local point cloud estimation (ablation) | MonoPSR · KITTI val1, moderate car, 0.5 IoU | AP_3D: 39.33 -> 41.06 | Helped |
| add projection alignment loss to local point cloud and depth losses (ablation) | MonoPSR · KITTI val1, moderate car, 0.5 IoU | AP_3D: 41.06 -> 41.71 | Helped |
Closest papers by meaning
- MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation (CVPR 2021 · similarity 0.92)
- MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priors (CVPR 2025 · similarity 0.90)
- Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection (AAAI 2022 · similarity 0.90)
- MonoDiff: Monocular 3D Object Detection and Pose Estimation with Diffusion Models (CVPR 2024 · similarity 0.89)
- MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders (NeurIPS 2024 · similarity 0.89)
- MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular Detection (CVPR 2025 · similarity 0.88)
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