InfoCD: A Contrastive Chamfer Distance Loss for Point Cloud Completion Fangzhou Lin 1,2 Y un Yue

A point cloud is a discrete set of data points sampled from a 3D geometric surface. Chamfer distance (CD) is a popular metric and training loss to measure the distances between point clouds, but also well known to be sensitive to outliers. We propose InfoCD, a novel contrastive Chamfer distance loss, and learn to spread the matched points to better align the distributions of point clouds. As such InfoCD leads to an improved surface similarity metric.