Research map / Detection and segmentation / 3D detection and driving
Boosting 3D Object Detection by Simulating Multimodality on Point Clouds
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add multimodal-to-single-modal distillation using response, sparse-voxel, voxel-to-point, and instance knowledge | Single-modality SSD* · nuScenes test | NDS: 67.3 -> 69.3 | Helped |
| Add multimodal-to-single-modal distillation using response, sparse-voxel, voxel-to-point, and instance knowledge | Single-modality SSD* · nuScenes test | mAP: 60.1 -> 62.9 | Helped |
| Use the distilled LiDAR-only S2M2-SSD instead of the LiDAR-image Multi-modality SSD* | Improved CenterPoint SSD · nuScenes test | NDS: 69.1 -> 69.3 | Helped |
| Use the distilled LiDAR-only S2M2-SSD instead of the LiDAR-image Multi-modality SSD* | Improved CenterPoint SSD · nuScenes test | mAP: 64.0 -> 62.9 | Hurt |
Closest papers by meaning
- SimDistill: Simulated Multi-Modal Distillation for BEV 3D Object Detection (AAAI 2024 · similarity 0.90)
- MsSVT: Mixed-scale Sparse Voxel Transformer for 3D Object Detection on Point Clouds (NeurIPS 2022 · similarity 0.89)
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection (CVPR 2018 · similarity 0.89)
- Not All Points Are Equal: Learning Highly Efficient Point-Based Detectors for 3D LiDAR Point Clouds (CVPR 2022 · similarity 0.89)
- Voxel or Pillar: Exploring Efficient Point Cloud Representation for 3D Object Detection (AAAI 2024 · similarity 0.89)
- LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection (ICLR 2024 · similarity 0.87)
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