{"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/shape-reconstruction-by-learning","title":"Shape Reconstruction by Learning Differentiable Surface Representations","arxiv_id":"1911.11227","date":"2019-11-25","proceeding":"CVPR 2020 6","authors":["Jan Bednarik","Shaifali Parashar","Erhan Gundogdu","Mathieu Salzmann","Pascal Fua"],"abstract":"Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric representations. Unfortunately, they offer no control over the deformations of the surface patches that form the ensemble and thus fail to prevent them from either overlapping or collapsing into single points or lines. As a consequence, computing shape properties such as surface normals and curvatures becomes difficult and unreliable. In this paper, we show that we can exploit the inherent differentiability of deep networks to leverage differential surface properties during training so as to prevent patch collapse and strongly reduce patch overlap. Furthermore, this lets us reliably compute quantities such as surface normals and curvatures. We will demonstrate on several tasks that this yields more accurate surface reconstructions than the state-of-the-art methods in terms of normals estimation and amount of collapsed and overlapped patches.","url_abs":"https://arxiv.org/abs/1911.11227v1","url_pdf":"https://arxiv.org/pdf/1911.11227v1.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":"shape-reconstruction-by-learning","repo_url":"https://github.com/bednarikjan/differential_surface_representation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1911.11227","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.11227"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/bednarikjan/differential_surface_representation","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":5},"by_repo_kind":{"official":{"samples":5,"ran":0,"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":0,"samples":[{"code_sha256_prefix":"dbe9bac4a16db280","entry":"identity","repo":"bednarikjan/differential_surface_representation","repo_kind":"official","path":"helpers.py","file_url":"https://github.com/bednarikjan/differential_surface_representation/blob/HEAD/helpers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"dbe9bac4a16db280"}},{"code_sha256_prefix":"1e6fef012d9e1b1c","entry":"load_conf","repo":"bednarikjan/differential_surface_representation","repo_kind":"official","path":"helpers.py","file_url":"https://github.com/bednarikjan/differential_surface_representation/blob/HEAD/helpers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1e6fef012d9e1b1c"}},{"code_sha256_prefix":"c19d0b28d138fddd","entry":"load_tf","repo":"bednarikjan/differential_surface_representation","repo_kind":"official","path":"preprocessing/shapenet_mesh_area.py","file_url":"https://github.com/bednarikjan/differential_surface_representation/blob/HEAD/preprocessing/shapenet_mesh_area.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c19d0b28d138fddd"}},{"code_sha256_prefix":"c5b668d3a94f44af","entry":"ls","repo":"bednarikjan/differential_surface_representation","repo_kind":"official","path":"helpers.py","file_url":"https://github.com/bednarikjan/differential_surface_representation/blob/HEAD/helpers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c5b668d3a94f44af"}},{"code_sha256_prefix":"8cb223bd7b70f6df","entry":"my_get_n_random_lines","repo":"bednarikjan/differential_surface_representation","repo_kind":"official","path":"data_loader.py","file_url":"https://github.com/bednarikjan/differential_surface_representation/blob/HEAD/data_loader.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8cb223bd7b70f6df"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}