Hi all,

We have just released the code for our ICCV 2019 paper on “Efficient Learning on Point Clouds with Basis Point Sets”:

https://github.com/sergeyprokudin/bps

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:

Basis point set encoding for point clouds.

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):

Mesh registration network using BPS features as input. Efficient 3D-convolutional models are also available.

Below are the key differences of BPS compared to standard occupancy voxels and truncated signed distance fields (TSDF):

• continuous global vectors instead of simple binary flags or local distances in the cells;
• smaller number of cells required to represent shape accurately;
• BPS cell arrangement could be different from a standard rectangular grid, allowing different types of convolutions;
• significant improvement in performance: simply substituting occupancy voxels with BPS directional vectors results in a +9% accuracy improvement of a VoxNet-like 3D-convolutional network on a ModelNet40 classification challenge.

Check our ICCV 2019 paper for more details.