Research map / Detection and segmentation / 3D detection and driving
Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes From 2D Ones in RGB-Depth Images
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use 2.5D image features to regress 3D boxes instead of analyzing 3D point clouds | Amodal 3D detection · NYUV2 | mAP: 36.3 -> 40.9 | Helped |
| Add normalized depth to color-image input (ablation) | Amodal 3D detection · NYUV2 | mAP: 30.0 -> 40.1 | Helped |
| Replace normalized depth with HHA depth embedding (ablation) | Amodal 3D detection · NYUV2 | mAP: 40.1 -> 36.2 | Hurt |
| Add context information (ablation) | Amodal 3D detection · NYUV2 | mAP: 40.1 -> 40.9 | Helped |
| Add learned 3D regression offsets (ablation) | Amodal 3D detection · NYUV2 | mAP: 15.5 -> 40.9 | Helped |
Closest papers by meaning
- ImOV3D: Learning Open Vocabulary Point Clouds 3D Object Detection from Only 2D Images (NeurIPS 2024 · similarity 0.89)
- Cubify Anything: Scaling Indoor 3D Object Detection (CVPR 2025 · similarity 0.88)
- In-Hand 3D Object Reconstruction from a Monocular RGB Video (AAAI 2024 · similarity 0.87)
- ImageNet3D: Towards General-Purpose Object-Level 3D Understanding (NeurIPS 2024 · similarity 0.87)
- Rooms from Motion: Un-posed Indoor 3D Object Detection as Localization and Mapping (NeurIPS 2025 · similarity 0.87)
- Amodal Ground Truth and Completion in the Wild (CVPR 2024 · similarity 0.87)
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