J
Jiaqi Yang
Researcher at Northwestern Polytechnical University
Publications - 63
Citations - 996
Jiaqi Yang is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Computer science & Feature (computer vision). The author has an hindex of 11, co-authored 49 publications receiving 516 citations. Previous affiliations of Jiaqi Yang include Huazhong University of Science and Technology & Industrial Technology Research Institute.
Papers
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Journal ArticleDOI
A fast and robust local descriptor for 3D point cloud registration
Jiaqi Yang,Zhiguo Cao,Qian Zhang +2 more
TL;DR: This paper proposes a novel local feature descriptor, called a local feature statistics histogram (LFSH), for efficient 3D point cloud registration, and an optimized sample consensus (OSAC) algorithm is developed to iteratively estimate the optimum transformation from point correspondences.
Journal ArticleDOI
TOLDI: An effective and robust approach for 3D local shape description
TL;DR: Experimental results and comparisons with the state-of-the-arts validate the effectiveness, robustness, high efficiency, and overall superiority of the TOLDI method for local shape description.
Proceedings ArticleDOI
NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences
TL;DR: In this article, a hierarchical network named NM-Net is proposed to extract and aggregate more reliable features from neighbors, which is insensitive to the order of correspondences and achieves the state-of-the-art performance on four datasets with various inlier ratios and varying numbers of feature consistencies.
Journal ArticleDOI
Toward the Repeatability and Robustness of the Local Reference Frame for 3D Shape Matching: An Evaluation.
Jiaqi Yang,Yang Xiao,Zhiguo Cao +2 more
TL;DR: Eight state-of-the-art LRF proposals are evaluated on six benchmarks with different data modalities and application contexts, and the robustness of each LRF to a variety of nuisances is assessed.
Journal ArticleDOI
Compatibility-Guided Sampling Consensus for 3-D Point Cloud Registration
Siwen Quan,Jiaqi Yang +1 more
TL;DR: An efficient and robust estimator called compatibility-guided sampling consensus (CG-SAC) to achieve accurate 3-D point cloud registration and proposes a new geometric constraint named the distance between salient points (DSP) to measure the compatibility of two correspondences.