Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
下圖Vote3Deep在KITTI上不可見點雲的檢測效果。 本文方法：在原生的3D特徵（native 3D representation）上執行一個稀疏卷積，接着是一個ReLU非線性，它返回一個新的稀疏3D特徵。這個過程可以像傳統CNN一樣重複和疊加，輸出層預測檢測分數。用NMS對重複檢測進行剪枝（3D中的NMS能夠更好地處理彼此背後的對象，因爲3D包圍框的重疊小於它們的2D投影）。 本文爲每個類
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- 1. Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient CNN論文綜述（2016）
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相關文章
- 1. Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient CNN論文綜述（2016）
- 2. PointPillars: Fast Encoders for 3D Object Detection from Point Clouds
- 3. Convolutional Neural Network - Object Detection
- 4. Fast Face-swap Using Convolutional Neural Networks
- 5. 【3D計算機視覺】Deep Hough Voting for 3D Object Detection in Point Clouds
- 6. PIXOR Real-time 3D Object Detection from Point Clouds
- 7. Convolutional Neural Networks（week 3）---Object detection
- 8. ShellNet:Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statics
- 9. 《論文閱讀》ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes
- 10. Point Cloud 2019