We propose 3DSmoothNet, a full workflow to match 3D point clouds with a siamese deep learning architecture and fully convolutional layers using a voxelized smoothed density value (SDV) representation. The latter is computed per interest point and aligned to the local reference frame (LRF) to achieve rotation invariance. Our compact, learned, rotation invariant 3D point cloud descriptor achieves 94.9% average recall on the 3DMatch benchmark data set, outperforming the state-of-the-art by more than 20 percent points with only 32 output dimensions. This very low output dimension allows for near real-time correspondence search with 0.1 ms per feature point on a standard PC. Our approach is sensor- and scene-agnostic because of SDV, LRF and learning highly descriptive features with fully convolutional layers. We show that 3DSmoothNet trained only on RGB-D indoor scenes of buildings achieves 79.0% average recall on laser scans of outdoor vegetation, more than double the performance of our closest, learningbased competitors.

提出了一种基于三维点云的全工作流程，利用卷积神经网络和全卷积层来匹配三维点云，利用兴趣点体素化光滑密度值 (SDV) 表示，从而实现旋转不变性，并在3DMatch基准数据集上取得了94.9%的平均召回率，性能比同类方法提高了20%以上，同时实现了高效的近实时的对应点搜索。

平滑密度下的 3D 点云匹配