Knowledge Commons of Institute of Automation,CAS
PuzzleNet: Boundary-Aware Feature Matching for Non-Overlapping 3D Point Clouds Assembly | |
Liu, Hao-Yu1,2; Guo, Jian-Wei1,2![]() ![]() ![]() | |
发表期刊 | JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
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ISSN | 1000-9000 |
2023-06-01 | |
卷号 | 38期号:3页码:492-509 |
摘要 | We address the 3D shape assembly of multiple geometric pieces without overlaps, a scenario often encountered in 3D shape design, field archeology, and robotics. Existing methods depend on strong assumptions on the number of shape pieces and coherent geometry or semantics of shape pieces. Despite raising attention to 3D registration with complex or low overlapping patterns, few methods consider shape assembly with rare overlaps. To address this problem, we present a novel framework inspired by solving puzzles, named PuzzleNet, which conducts multi-task learning by leveraging both 3D alignment and boundary information. Specifically, we design an end-to-end neural network based on a point cloud transformer with two-way branches for estimating rigid transformation and predicting boundaries simultaneously. The framework is then naturally extended to reassemble multiple pieces into a full shape by using an iterative greedy approach based on the distance between each pair of candidate-matched pieces. To train and evaluate PuzzleNet, we construct two datasets, named DublinPuzzle and ModelPuzzle, based on a real-world urban scan dataset (DublinCity) and a synthetic CAD dataset (ModelNet40) respectively. Experiments demonstrate our effectiveness in solving 3D shape assembly for multiple pieces with arbitrary geometry and inconsistent semantics. Our method surpasses state-of-the-art algorithms by more than 10 times in rotation metrics and four times in translation metrics. |
关键词 | shape assembly 3D registration geometric learning boundary feature point cloud |
DOI | 10.1007/s11390-023-3127-8 |
关键词[WOS] | REGISTRATION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[62172416] ; National Natural Science Foundation of China[U21A20515] ; National Natural Science Foundation of China[62172415] ; National Natural Science Foundation of China[62271467] ; National Natural Science Foundation of China[2022131] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[U22B2034] |
项目资助者 | National Natural Science Foundation of China ; Youth Innovation Promotion Association of the Chinese Academy of Sciences |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Hardware & Architecture ; Computer Science, Software Engineering |
WOS记录号 | WOS:001089311400003 |
出版者 | SPRINGER SINGAPORE PTE LTD |
七大方向——子方向分类 | 计算机图形学与虚拟现实 |
国重实验室规划方向分类 | 环境多维感知 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/54458 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Guo, Jian-Wei |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Liu, Hao-Yu,Guo, Jian-Wei,Jiang, Hai-Yong,et al. PuzzleNet: Boundary-Aware Feature Matching for Non-Overlapping 3D Point Clouds Assembly[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2023,38(3):492-509. |
APA | Liu, Hao-Yu,Guo, Jian-Wei,Jiang, Hai-Yong,Liu, Yan-Chao,Zhang, Xiao-Peng,&Yan, Dong-Ming.(2023).PuzzleNet: Boundary-Aware Feature Matching for Non-Overlapping 3D Point Clouds Assembly.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,38(3),492-509. |
MLA | Liu, Hao-Yu,et al."PuzzleNet: Boundary-Aware Feature Matching for Non-Overlapping 3D Point Clouds Assembly".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38.3(2023):492-509. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
2023-JCST-PuzzleNet-(7448KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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