Research map / Detection and segmentation / 3D detection and driving
Triangulation Learning Network: From Monocular to Stereo 3D Object Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add stereo TLNet to the monocular baseline | Ours (baseline) · KITTI val Car Moderate, AP3D IoU=0.5 | AP3D: 33.98 -> 43.71 | Helped |
| replace channel reweighting with concatenation (ablation) | TLNet · KITTI val Car Moderate, AP3D IoU=0.3 | AP3D: 63.36 -> 60.81 | Hurt |
| replace channel reweighting with element-wise addition (ablation) | TLNet · KITTI val Car Moderate, AP3D IoU=0.3 | AP3D: 63.36 -> 59.89 | Hurt |
| replace channel reweighting with concatenation (ablation) | TLNet · KITTI val Car Moderate, AP3D IoU=0.5 | AP3D: 43.71 -> 41.20 | Hurt |
| replace channel reweighting with element-wise addition (ablation) | TLNet · KITTI val Car Moderate, AP3D IoU=0.5 | AP3D: 43.71 -> 41.61 | Hurt |
| replace channel reweighting with concatenation (ablation) | TLNet · KITTI val Car Moderate, AP3D IoU=0.7 | AP3D: 14.26 -> 11.63 | Hurt |
| replace channel reweighting with element-wise addition (ablation) | TLNet · KITTI val Car Moderate, AP3D IoU=0.7 | AP3D: 14.26 -> 13.59 | Hurt |
Closest papers by meaning
- Multi-Level Fusion Based 3D Object Detection From Monocular Images (CVPR 2018 · similarity 0.91)
- Categorical Depth Distribution Network for Monocular 3D Object Detection (CVPR 2021 · similarity 0.90)
- Learning Depth-Guided Convolutions for Monocular 3D Object Detection (CVPR 2020 · similarity 0.90)
- MonoCD: Monocular 3D Object Detection with Complementary Depths (CVPR 2024 · similarity 0.88)
- MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders (NeurIPS 2024 · similarity 0.88)
- MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priors (CVPR 2025 · similarity 0.88)
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