Learning on (3D) point clouds is vital for a broad range of emerging applications such as autonomous driving, robot perception, VR/AR, gaming, and security. Such needs have increased recently due to the prevalence of 3D sensors such as LiDAR, 3D camera, and RGB-D. Point clouds consist of thousands to millions of points and are complementary to the traditional 2D cameras that we have been working on for years in the vision (or multimedia) community. 3D learning algorithms on point cloud data are new, and exciting, for numerous core problems such as 3D classification, detection, semantic segmentation, and face recognition. The tutorial covers the requirements of point cloud data, the background of capturing the data, 3D representations, emerging applications, core problems, state-of-the art learning algorithms (e.g., voxel-based, point-based, etc.), and future research opportunities. We will also showcase our leading work in several 3D benchmarks such as ScanNet, KITTI, etc.

The lecturing slides are also available at http://bit.ly/2MG2PGf