VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
論文： 在3D點雲中準確檢測對象是自主導航，客房服務機器人和增強/虛擬現實的中心問題。爲了將高度稀疏的LiDAR點雲與區域提議網絡（RPN）進行接口，大多數現有工作都集中在手工製作的特徵表示上，例如鳥瞰圖。在這項工作中，消除了對3D點雲進行手動特徵工程的需求，並提出了VoxelNet，這是一種通用的3D檢測網絡，它將特徵提取和邊界框預測統一爲一個端到端可訓練的單階段深層網絡。將點雲轉換爲等距的3D
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- 1. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 2. VoxelNet：End-to-End Learning for Point Cloud Based 3D Object Detection閱讀筆記
- 3. 3D Object Detection——Voxel based methods
- 4. 三維目標檢測論文閱讀：VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 5. 【文章閱讀】STD：Sparse-to-Dense 3D Object Detector for Point Cloud
- 6. Structure Aware Single-stage 3D Object Detection from Point Cloud
- 7. 3d fully convolutional network for vehicle detection in point cloud
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- 9. PointPillars: Fast Encoders for 3D Object Detection from Point Clouds
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相關文章
- 1. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 2. VoxelNet：End-to-End Learning for Point Cloud Based 3D Object Detection閱讀筆記
- 3. 3D Object Detection——Voxel based methods
- 4. 三維目標檢測論文閱讀：VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 5. 【文章閱讀】STD：Sparse-to-Dense 3D Object Detector for Point Cloud
- 6. Structure Aware Single-stage 3D Object Detection from Point Cloud
- 7. 3d fully convolutional network for vehicle detection in point cloud
- 8. RepPoints: Point Set Representation for Object Detection
- 9. PointPillars: Fast Encoders for 3D Object Detection from Point Clouds
- 10. Point Cloud 2019