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.

To speed up ACL, this paper proposes a robustness-aware coreset selection (RCS) method.

Stochastic gradient descent (SGD) algorithm is the method of choice in many machine learning tasks thanks to its scalability and efficiency in dealing with large-scale problems. In this paper, we focus on the shuffling version of SGD which matches the mainstream practical heuristics. We show the convergence to a global solution of shuffling SGD for a class of non-convex functions under over-parameterized settings.

To evaluate recovered data from Orthogonal Projection, we consider an additional architecture: ViT.

However, current data characterization methods are largely focused on classification settings, with regression settings largely understudied.