Research map / Point clouds / Segmentation and detection
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace hand-crafted BEV features with learned VoxelNet point-cloud features | VoxelNet versus HC-baseline · KITTI validation, car, pedestrian, and cyclist BEV detection | AP: 88.26 -> 89.60; 78.42 -> 84.81; 77.66 -> 78.57; 58.96 -> 65.95; 53.79 -> 61.05; 51.47 -> 56.98; 63.63 -> 74.41; 42.75 -> | Helped |
| Replace hand-crafted BEV features with learned VoxelNet point-cloud features | VoxelNet versus HC-baseline · KITTI validation, car, pedestrian, and cyclist 3D detection | AP: 71.73 -> 81.97; 59.75 -> 65.46; 55.69 -> 62.85; 43.95 -> 57.86; 40.18 -> 53.42; 37.48 -> 48.87; 55.35 -> 67.17; 36.07 -> | Helped |
| Increase hand-crafted baseline height channels from 4 to 16 | HC-baseline · KITTI detection | AP | Helped |
| Increase hand-crafted baseline height channels beyond 16 | HC-baseline · KITTI detection | AP | No clear effect |
Closest papers by meaning
- HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection (CVPR 2020 · similarity 0.94)
- VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking (CVPR 2023 · similarity 0.93)
- JPV-Net: Joint Point-Voxel Representations for Accurate 3D Object Detection (AAAI 2022 · similarity 0.93)
- Point Density-Aware Voxels for LiDAR 3D Object Detection (CVPR 2022 · similarity 0.93)
- HVPR: Hybrid Voxel-Point Representation for Single-Stage 3D Object Detection (CVPR 2021 · similarity 0.92)
- Voxel or Pillar: Exploring Efficient Point Cloud Representation for 3D Object Detection (AAAI 2024 · similarity 0.92)
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