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[Research] Efficient Learning on Point Clouds with Basis Point Sets

[Research] Efficient Learning on Point Clouds with Basis Point Sets

Hi all,

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:

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.

submitted by /u/___sergey
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