The Perfect Match: 3D Point Cloud Matching with Smoothed Densities

CVPR 2019 Zan GojcicCaifa ZhouJan D. WegnerAndreas Wieser

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... (read more)

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