47 Papers
9 Citations
Honghua Chen is an academic researcher from Nanjing University of Aeronautics and Astronautics. The author has contributed to research in topics: Computer science & Point cloud. The author has an hindex of 4, co-authored 11 publications.
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Papers
Geometry and Learning Co-Supported Normal Estimation for Unstructured Point Cloud
Haoran Zhou,Honghua Chen,Yidan Feng,Qiong Wang,Jing Qin,Haoran Xie,Fu Lee Wang,Mingqiang Wei,Jun Wang +8 more
- 14 Jun 2020
TL;DR: Qualitative and quantitative evaluations demonstrate the clear improvements of the results over both traditional methods and learning-based methods, in terms of estimation accuracy and feature recovery, in the co-support of geometric estimator and deep learning.
RePCD-Net: Feature-Aware Recurrent Point Cloud Denoising Network
TL;DR: Extensive qualitative and quantitative evaluations demonstrate the effectiveness and superiority of the RePCD-Net method over state-of-the-arts, in terms of noise removal and feature preservation.
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Aircraft Skin Rivet Detection Based on 3D Point Cloud via Multiple Structures Fitting
TL;DR: This paper presents an automated density-aware multiple-structure fitting algorithm to perform rivet detection based on a 3D point cloud and demonstrates that the proposed algorithm achieves significant superiority over several state-of-the-art model fitting methods on the real scanned point cloud via experimental results.
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ImLoveNet: Misaligned Image-supported Registration Network for Low-overlap Point Cloud Pairs
Honghua Chen,Zeyong Wei,Yabin Xu,Mingqiang Wei,Jun Wang +4 more
- 02 Jul 2022
TL;DR: Li et al. as discussed by the authors proposed a misaligned image supported registration network for low-overlap point cloud pairs, dubbed ImLoveNet, which first learns triple deep features across different modalities and then exports these features to a two-stage classifier, for progressively obtaining the high-confidence overlap region between the two point clouds.
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Part-in-whole point cloud registration for aircraft partial scan automated localization
TL;DR: A Multi-Descriptor Voting (MDV) scheme and a Seam Structure Aware ICP (SSA-ICP) for fine registration to eliminate the localization ambiguity in local regions, based on the detected seam points are proposed.
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