{"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/learning-multi-resolution-functional-maps","title":"Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching","arxiv_id":"2210.06373","date":"2022-10-12","proceeding":null,"authors":["Lei LI","Nicolas Donati","Maks Ovsjanikov"],"abstract":"In this work, we present a novel non-rigid shape matching framework based on multi-resolution functional maps with spectral attention. Existing functional map learning methods all rely on the critical choice of the spectral resolution hyperparameter, which can severely affect the overall accuracy or lead to overfitting, if not chosen carefully. In this paper, we show that spectral resolution tuning can be alleviated by introducing spectral attention. Our framework is applicable in both supervised and unsupervised settings, and we show that it is possible to train the network so that it can adapt the spectral resolution, depending on the given shape input. More specifically, we propose to compute multi-resolution functional maps that characterize correspondence across a range of spectral resolutions, and introduce a spectral attention network that helps to combine this representation into a single coherent final correspondence. Our approach is not only accurate with near-isometric input, for which a high spectral resolution is typically preferred, but also robust and able to produce reasonable matching even in the presence of significant non-isometric distortion, which poses great challenges to existing methods. We demonstrate the superior performance of our approach through experiments on a suite of challenging near-isometric and non-isometric shape matching benchmarks.","url_abs":"https://arxiv.org/abs/2210.06373v1","url_pdf":"https://arxiv.org/pdf/2210.06373v1.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":"learning-multi-resolution-functional-maps","repo_url":"https://github.com/craigleili/attentivefmaps","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2210.06373","atlas_url":"https://app.syntology.ai/?focus=2210.06373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06373"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/craigleili/attentivefmaps","reach":null}],"summary":{"ran":3,"ran_honours":1,"ran_draft_wrong":1,"unverified":2},"by_repo_kind":{"official":{"samples":7,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":7,"samples":[{"code_sha256_prefix":"652437774a3e247a","entry":"FeatureSTN","repo":"craigleili/attentivefmaps","repo_kind":"official","path":"models/attnfmaps.py","file_url":"https://github.com/craigleili/attentivefmaps/blob/HEAD/models/attnfmaps.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"652437774a3e247a"}},{"code_sha256_prefix":"fe83f084e5988636","entry":"PointConvBlock","repo":"craigleili/attentivefmaps","repo_kind":"official","path":"models/attnfmaps.py","file_url":"https://github.com/craigleili/attentivefmaps/blob/HEAD/models/attnfmaps.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"fe83f084e5988636"}},{"code_sha256_prefix":"fd2179bafdb306a6","entry":"SEBlock","repo":"craigleili/attentivefmaps","repo_kind":"official","path":"models/attnfmaps.py","file_url":"https://github.com/craigleili/attentivefmaps/blob/HEAD/models/attnfmaps.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"fd2179bafdb306a6"}},{"code_sha256_prefix":"4ce377a2a1ab602e","entry":"load_corr_preds","repo":"craigleili/attentivefmaps","repo_kind":"official","path":"eval_corr.py","file_url":"https://github.com/craigleili/attentivefmaps/blob/HEAD/eval_corr.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4ce377a2a1ab602e"}},{"code_sha256_prefix":"f670532e0a4f5a55","entry":"pdists","repo":"craigleili/attentivefmaps","repo_kind":"official","path":"models/attnfmaps.py","file_url":"https://github.com/craigleili/attentivefmaps/blob/HEAD/models/attnfmaps.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f670532e0a4f5a55"}},{"code_sha256_prefix":"de7dc6f53cf5d7e4","entry":"SpectralAttentionNet","repo":"craigleili/attentivefmaps","repo_kind":"official","path":"models/attnfmaps.py","file_url":"https://github.com/craigleili/attentivefmaps/blob/HEAD/models/attnfmaps.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"de7dc6f53cf5d7e4"}},{"code_sha256_prefix":"0b612b80cb104f60","entry":"farthest_point_sampling","repo":"craigleili/attentivefmaps","repo_kind":"official","path":"models/attnfmaps.py","file_url":"https://github.com/craigleili/attentivefmaps/blob/HEAD/models/attnfmaps.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0b612b80cb104f60"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}