{"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/diffusion-3d-features-diff3f-decorating","title":"Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features","arxiv_id":"2311.17024","date":"2023-11-28","proceeding":"CVPR 2024 1","authors":["Niladri Shekhar Dutt","Sanjeev Muralikrishnan","Niloy J. Mitra"],"abstract":"We present Diff3F as a simple, robust, and class-agnostic feature descriptor that can be computed for untextured input shapes (meshes or point clouds). Our method distills diffusion features from image foundational models onto input shapes. Specifically, we use the input shapes to produce depth and normal maps as guidance for conditional image synthesis. In the process, we produce (diffusion) features in 2D that we subsequently lift and aggregate on the original surface. Our key observation is that even if the conditional image generations obtained from multi-view rendering of the input shapes are inconsistent, the associated image features are robust and, hence, can be directly aggregated across views. This produces semantic features on the input shapes, without requiring additional data or training. We perform extensive experiments on multiple benchmarks (SHREC'19, SHREC'20, FAUST, and TOSCA) and demonstrate that our features, being semantic instead of geometric, produce reliable correspondence across both isometric and non-isometrically related shape families. Code is available via the project page at https://diff3f.github.io/","url_abs":"https://arxiv.org/abs/2311.17024v2","url_pdf":"https://arxiv.org/pdf/2311.17024v2.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":"diffusion-3d-features-diff3f-decorating","repo_url":"https://github.com/niladridutt/Diffusion-3D-Features","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-dense-shape-correspondence","task_name":"3D Dense Shape Correspondence"},{"task_slug":"3d-part-segmentation","task_name":"3D Part Segmentation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-dense-shape-correspondence-on-shrec-19","task":"3D Dense Shape Correspondence","dataset":"SHREC'19","model":"Diffusion 3D Features (Zero-shot)","rank_in_archive_order":1,"of":11,"metrics":{"Accuracy at 1%":"26.4","Euclidean Mean Error (EME)":"1.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.17024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17024"}},"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/niladridutt/Diffusion-3D-Features","reach":null}],"summary":{"ran_fixture":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"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":"577e0252d69ebbb8","entry":"arange_pixels","repo":"niladridutt/Diffusion-3D-Features","repo_kind":"official","path":"diff3f.py","file_url":"https://github.com/niladridutt/Diffusion-3D-Features/blob/HEAD/diff3f.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"577e0252d69ebbb8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}