[Research] Efficient Learning on Point Clouds with Basis Point Sets
We have just released the code for our ICCV 2019 paper on “Efficient Learning on Point Clouds with Basis Point Sets”:
We present basis point sets (BPS), a simple and efficient method for encoding 3D point clouds into fixed-length representations.
The method is based on a following simple idea: select k fixed points in space and compute vectors from these basis points to the nearest points in a point cloud; use these vectors (or simply their norms) as features:
The basis points are kept fixed for all the point clouds in the dataset, providing a fixed representation of every point cloud as a vector. This representation can then be used as input to arbitrary machine learning methods, in particular it can be used as input to off-the-shelf neural networks.
Below is the example of a simple model using BPS features as input for the task of mesh registration over a noisy scan (you can check the resulting alignments here):
Below are the key differences of BPS compared to standard occupancy voxels and truncated signed distance fields (TSDF):
Check our ICCV 2019 paper for more details.