Haiping Wang
Wuhan University
13 Papers
Haiping Wang is an academic researcher from Wuhan University. The author has contributed to research in topics: Computer science & RANSAC. The author has an hindex of 1, co-authored 2 publications.
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Papers
RoReg: Pairwise Point Cloud Registration With Oriented Descriptors and Local Rotations
Haiping Wang,Yuan Liu,Qingyong Hu,Bing Wang,Jianguo Chen,Zhen Dong,Yulan Guo,Wenping Wang,Bisheng Yang +8 more
TL;DR: RoReg as discussed by the authors exploits oriented descriptors and estimated local rotations in the whole registration pipeline, including feature description, feature detection, feature matching, and transformation estimation, and achieves state-of-the-art performance.
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CG-SSD: Corner Guided Single Stage 3D Object Detection from LiDAR Point Cloud
TL;DR: Wang et al. as discussed by the authors proposed a corner-guided anchor-free single-stage 3D object detection model (CG-SSD) to estimate the locations of partially visible and invisible corners to obtain a more accurate object feature representation, especially for small or partial occluded objects.
•Posted Content
You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors
TL;DR: In this paper, a local descriptor-based framework, called You Only Hypothesize Once (YOHO), is proposed for the registration of two unaligned point clouds, which achieves the rotation invariance by recent technologies of group equivariant feature learning.
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A Probabilistic Method for Fractured Cultural Relics Automatic Reassembly
TL;DR: In this article, a probabilistic method for fractured cultural relics automatic reassembly is proposed to solve the problem in terms of good accuracy, efficiency, and robustness, which is very challenging to automatically reassemble a large collection of fragile cultural objects.
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SparseDC: Depth completion from sparse and non-uniform inputs
Chen Long,Wenxiao Zhang,Zhe Chen,Haiping Wang,Yuan Liu,Peiling Tong,Bisheng Yang +6 more
TL;DR: SparseDC, a depth completion model, handles sparse and non-uniform inputs by filling unstable depth features with image features and utilizing an uncertainty-based feature fusion module, outperforming the SOTA method CFormer on NYU Depth dataset with reduced parameters.
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