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VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
時間 2020-12-30
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3D檢測
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VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection 簡介 3D目標檢測整體框架 特徵學習網絡 卷積中間層 區域提出網絡(RPN) loss 簡介 3D點雲中物體的精確檢測是許多應用中的一個核心問題,爲了將高度稀疏的激光雷達點雲與區域建議網絡(RPN)進行接口。處理LIDAR點雲的難點:點雲稀疏、點雲密度變化
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VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
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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
>>更多相關文章<<