{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/robust-partial-to-partial-point-cloud","title":"Robust Partial-to-Partial Point Cloud Registration in a Full Range","arxiv_id":"2111.15606","date":"2021-11-30","proceeding":null,"authors":["Liang Pan","Zhongang Cai","Ziwei Liu"],"abstract":"Point cloud registration for 3D objects is a challenging task due to sparse and noisy measurements, incomplete observations and large transformations. In this work, we propose \\textbf{G}raph \\textbf{M}atching \\textbf{C}onsensus \\textbf{Net}work (\\textbf{GMCNet}), which estimates pose-invariant correspondences for full-range Partial-to-Partial point cloud Registration (PPR) in the object-level registration scenario. To encode robust point descriptors, \\textbf{1)} we first comprehensively investigate transformation-robustness and noise-resilience of various geometric features. \\textbf{2)} Then, we employ a novel {T}ransformation-robust {P}oint {T}ransformer (\\textbf{TPT}) module to adaptively aggregate local features regarding the structural relations, which takes advantage from both handcrafted rotation-invariant ({\\textit{RI}}) features and noise-resilient spatial coordinates. \\textbf{3)} Based on a synergy of hierarchical graph networks and graphical modeling, we propose the {H}ierarchical {G}raphical {M}odeling (\\textbf{HGM}) architecture to encode robust descriptors consisting of i) a unary term learned from {\\textit{RI}} features; and ii) multiple smoothness terms encoded from neighboring point relations at different scales through our TPT modules. Moreover, we construct a challenging PPR dataset (\\textbf{MVP-RG}) based on the recent MVP dataset that features high-quality scans. Extensive experiments show that GMCNet outperforms previous state-of-the-art methods for PPR. Notably, GMCNet encodes point descriptors for each point cloud individually without using cross-contextual information, or ground truth correspondences for training. Our code and datasets are available at: https://github.com/paul007pl/GMCNet.","url_abs":"https://arxiv.org/abs/2111.15606v2","url_pdf":"https://arxiv.org/pdf/2111.15606v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"robust-partial-to-partial-point-cloud","repo_url":"https://github.com/paul007pl/gmcnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-matching","task_name":"Graph Matching"},{"task_slug":"point-cloud-registration","task_name":"Point Cloud Registration"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2111.15606","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}