Small object detection for 3D point cloud is a challenging problem because of two limitations: (1) The sparsity clouds significantly increases the difficulty perceiving small objects. (2) occlusion objects can easily break shape their clouds. To alleviate these problems, we design point-based network PSA-Det3D which mainly consists pillar set abstraction (PSA) and foreground compensation (FPC)....

3D object matching and registration on point clouds are widely used in computer vision. However, most existing cloud methods have limitations handling non-rigid sets or topology changes (e.g. connections separations). As a result, critical characteristics such as large inter-frame motions of the may not be accurately captured. This paper proposes statistical algorithm for registration, addressi...

This paper studies a panel data regression setting, where a break occurs at a unknown common date. In this paper, we establish the consistency and rate of convergence of the change point estimator. The break date can be estimated consistently both in …xed time horizon and large panels, which indicates that the structural change can be well detected even in short panels. Furthermore, the limitin...