A set of points in 3D space (point cloud) is a way of representing surface shape of an object e.g. for neural network-based classification approaches. Depending on the desired application, a good point cloud covers the whole object surface uniformly, without large clusters of points near each other, or, given a limited/fixed number of points, complex parts of the object (edges, curved parts) are sampled more densely, omitting points on flat surfaces, thus focusing on the “important” parts of the object. Point clouds can be produced by LiDAR scanners or generated from polygonal meshes by sampling the surface.
There are multiple possibilities how to generate a point cloud, e.g. uniformly sampling the object surface or using a low-discrepancy sequence; and post-processing techniques such as farthest point sampling to remove some of the sampled point. The goal of this project is to explore and possibly improve existing mesh-to-point-cloud conversion methods e.g. by making farthest point sampling more robust to outlier points, using a local-feature-prediction neural network such as PCPNet for selection of important points or combining multiple point sampling/selection methods.