|Title:||Learning from 3D (Point cloud) data||Authors:||Hsu, Winston H.||Keywords:||3D imaging | Autonomous Driving | LiDAR | Object Detection | Point Clouds | PointNet | RGB-D | Robot | VoxelNet||Issue Date:||15-Oct-2019||Source:||MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia||Abstract:||
© 2019 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-6889-6/19/10. Learning on (3D) point clouds is vital for a broad range of emerging applications such as autonomous driving, robot perception, augmented reality, 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; They contain rich information and are complementary to the traditional 2D cameras that we have been working on for years in the multimedia (or vision) 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. 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, and future research opportunities.
|Appears in Collections:||資訊工程學系|
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