Research map / Detection and segmentation / 3D detection and driving
GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train with GrooMeD-NMS rather than applying NMS only at inference | M3D-RPN-based monocular 3D detector · KITTI Val 1 Cars, 3D AP40 IoU≥0.7 Moderate | AP3D40: 13.55 -> 14.32 | Helped |
| Add a confidence head (ablation) | GrooMeD-NMS detector · KITTI Val 1 Cars, 3D AP40 IoU≥0.7 Moderate | AP3D40: 12.10 -> 13.89 | Helped |
| Add NMS training (ablation) | Confidence-head detector · KITTI Val 1 Cars, 3D AP40 IoU≥0.7 Moderate | AP3D40: 13.89 -> 14.32 | Helped |
| Use confidence scores rather than classification scores for NMS (ablation) | GrooMeD-NMS detector · KITTI Val 1 Cars, 3D AP40 IoU≥0.7 Moderate | AP3D40: 11.21 -> 14.32 | Helped |
| Add warmup initialization (ablation) | GrooMeD-NMS detector · KITTI Val 1 Cars, 3D AP40 IoU≥0.7 Moderate | AP3D40: 11.68 -> 14.32 | Helped |
| Use linear rather than exponential pruning (τ=1) (ablation) | GrooMeD-NMS · KITTI Val 1 Cars, 3D AP40 IoU≥0.7 Moderate | AP3D40: 9.26 -> 14.32 | Helped |
| Use linear rather than exponential pruning (τ=0.5) (ablation) | GrooMeD-NMS · KITTI Val 1 Cars, 3D AP40 IoU≥0.7 Moderate | AP3D40: 13.85 -> 14.32 | Helped |
| Use linear rather than exponential pruning (τ=0.1) (ablation) | GrooMeD-NMS · KITTI Val 1 Cars, 3D AP40 IoU≥0.7 Moderate | AP3D40: 13.79 -> 14.32 | Helped |
| Use linear rather than sigmoidal pruning (τ=0.1) (ablation) | GrooMeD-NMS · KITTI Val 1 Cars, 3D AP40 IoU≥0.7 Moderate | AP3D40: 13.21 -> 14.32 | Helped |
| Add grouping (ablation) | GrooMeD-NMS · KITTI Val 1 Cars, 3D AP40 IoU≥0.7 Moderate | AP3D40: 13.91 -> 14.32 | Helped |
| Add masking to grouped NMS (ablation) | GrooMeD-NMS · KITTI Val 1 Cars, 3D AP40 IoU≥0.7 Moderate | AP3D40: 13.74 -> 14.32 | Helped |
| Use Imagewise AP rather than Vanilla AP loss (ablation) | GrooMeD-NMS · KITTI Val 1 Cars, 3D AP40 IoU≥0.7 Moderate | AP3D40: 13.73 -> 14.32 | Helped |
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- MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders (NeurIPS 2024 · similarity 0.86)
- MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priors (CVPR 2025 · similarity 0.86)
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- UniMODE: Unified Monocular 3D Object Detection (CVPR 2024 · similarity 0.86)
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