Point cloud super-resolution is a fundamental problem for 3D reconstruction and 3D data understanding. It takes a low-resolution (LR) point cloud as input and generates a high-resolution (HR) point cloud with rich details
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Point clouds acquired from range scans are often sparse, noisy, and non-uniform.
The key idea of the proposed network is to exploit the local similarity of point cloud and the analogy between LR input and HR output.