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Yulan Guo
Researcher at National University of Defense Technology
Publications - 196
Citations - 9683
Yulan Guo is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Computer science & Point cloud. The author has an hindex of 30, co-authored 164 publications receiving 5012 citations. Previous affiliations of Yulan Guo include Chinese Academy of Sciences & University of Western Australia.
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Journal ArticleDOI
Deep Learning for 3D Point Clouds: A Survey
TL;DR: This paper presents a comprehensive review of recent progress in deep learning methods for point clouds, covering three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation.
Proceedings ArticleDOI
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Qingyong Hu,Bo Yang,Linhai Xie,Stefano Rosa,Yulan Guo,Zhihua Wang,Niki Trigoni,Andrew Markham +7 more
TL;DR: This paper introduces RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds, and introduces a novel local feature aggregation module to progressively increase the receptive field for each 3D point, thereby effectively preserving geometric details.
Journal ArticleDOI
3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey
TL;DR: This paper presents a comprehensive survey of existing local surface feature based 3D object recognition methods and enlists a number of popular and contemporary databases together with their relevant attributes.
Journal ArticleDOI
A Comprehensive Performance Evaluation of 3D Local Feature Descriptors
TL;DR: This paper compares ten popular local feature descriptors in the contexts of 3D object recognition, 3D shape retrieval, and 3D modeling and presents the performance results of these descriptors when combined with different 3D keypoint detection methods.
Journal ArticleDOI
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition
TL;DR: Rotational Projection Statistics (RoPS) as discussed by the authors is a feature descriptor that is obtained by rotationally projecting the neighboring points of a feature point onto 2D planes and calculating a set of statistics including low-order central moments and entropy of the distribution of these projected points.