3D point cloud segmentation: A survey

By Anh Viet Nguyen, Hoai Bac Le. IEEE Conference on Robotics, Automation and Mechatronics, 2013

432 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep Learning Advances in Computer Vision with 3D Data (2017, methodology): “Finally, a survey of 3D point cloud segmentation methods is presented in Nguyen and Le [2013].”
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review (2020, background): “These tasks are described as follows respectively: • 3D point cloud semantic segmentation: Point cloud segmentation is the process to cluster the input data into several homogeneous regions, where points in the same region have the identical attributes [39].”
Metaverse: Perspectives from graphics, interactions and visualization (2022, methodology): “The region growth (Pu et al., 2006) and RANSAC (Liu et al., 2016) are two efficient segmentation methods (Nguyen and Le, 2013).”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “Besides those works reviewed in [30], the attribute based algorithm proposed in [56] demonstrated that it was capable of segmenting pole-like objects, which was considered as challenging due to its thin feature.”