{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/multi-view-3d-reconstruction/papers/ran/1","list_of":"/task/multi-view-3d-reconstruction","task":"Multi-View 3D Reconstruction","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,11],"of":11,"counts":{"archive_papers_tagged":60,"with_a_code_link":36,"where_syntology_ran_a_sample":11,"not_listed_spam_title":0,"listed":60,"listed_where_code_ran":11,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":10,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":10,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/multi-view-3d-reconstruction/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/lotus-diffusion-based-visual-foundation-model","slug":"lotus-diffusion-based-visual-foundation-model","title":"Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction","date":"2024-09-26","arxiv_id":"2409.18124","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":3,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/lotus-diffusion-based-visual-foundation-model#ran","syntology_url":"https://syntology.ai/paper/2409.18124","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.18124"}},"official":null}},{"url":"/paper/efm3d-a-benchmark-for-measuring-progress","slug":"efm3d-a-benchmark-for-measuring-progress","title":"EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models","date":"2024-06-14","arxiv_id":"2406.10224","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efm3d-a-benchmark-for-measuring-progress#ran","syntology_url":"https://syntology.ai/paper/2406.10224","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.10224"}},"official":{"repos":["facebookresearch/efm3d"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/supernormal-neural-surface-reconstruction-via","slug":"supernormal-neural-surface-reconstruction-via","title":"SuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration","date":"2023-12-08","arxiv_id":"2312.04803","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/supernormal-neural-surface-reconstruction-via#ran","syntology_url":"https://syntology.ai/paper/2312.04803","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.04803"}},"official":{"repos":["CyberAgentAILab/SuperNormal"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/porf-pose-residual-field-for-accurate-neural","slug":"porf-pose-residual-field-for-accurate-neural","title":"PoRF: Pose Residual Field for Accurate Neural Surface Reconstruction","date":"2023-10-11","arxiv_id":"2310.07449","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/porf-pose-residual-field-for-accurate-neural#ran","syntology_url":"https://syntology.ai/paper/2310.07449","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07449"}},"official":{"repos":["ActiveVisionLab/porf"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/objectsdf-improved-object-compositional","slug":"objectsdf-improved-object-compositional","title":"ObjectSDF++: Improved Object-Compositional Neural Implicit Surfaces","date":"2023-08-15","arxiv_id":"2308.07868","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":5,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/objectsdf-improved-object-compositional#ran","syntology_url":"https://syntology.ai/paper/2308.07868","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.07868"}},"official":{"repos":["qianyiwu/objectsdf_plus"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/monosdf-exploring-monocular-geometric-cues","slug":"monosdf-exploring-monocular-geometric-cues","title":"MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction","date":"2022-06-01","arxiv_id":"2206.00665","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":4,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/monosdf-exploring-monocular-geometric-cues#ran","syntology_url":"https://syntology.ai/paper/2206.00665","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.00665"}},"official":null}},{"url":"/paper/legoformer-transformers-for-block-by-block","slug":"legoformer-transformers-for-block-by-block","title":"LegoFormer: Transformers for Block-by-Block Multi-view 3D Reconstruction","date":"2021-06-23","arxiv_id":"2106.12102","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/legoformer-transformers-for-block-by-block#ran","syntology_url":"https://syntology.ai/paper/2106.12102","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.12102"}},"official":{"repos":["faridyagubbayli/LegoFormer"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/planar-prior-assisted-patchmatch-multi-view","slug":"planar-prior-assisted-patchmatch-multi-view","title":"Planar Prior Assisted PatchMatch Multi-View Stereo","date":"2019-12-26","arxiv_id":"1912.11744","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/planar-prior-assisted-patchmatch-multi-view#ran","syntology_url":"https://syntology.ai/paper/1912.11744","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.11744"}},"official":{"repos":["GhiXu/ACMP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/differentiable-volumetric-rendering-learning","slug":"differentiable-volumetric-rendering-learning","title":"Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision","date":"2019-12-16","arxiv_id":"1912.07372","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/differentiable-volumetric-rendering-learning#ran","syntology_url":"https://syntology.ai/paper/1912.07372","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.07372"}},"official":{"repos":["autonomousvision/differentiable_volumetric_rendering"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pix2vox-context-aware-3d-reconstruction-from","slug":"pix2vox-context-aware-3d-reconstruction-from","title":"Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images","date":"2019-01-31","arxiv_id":"1901.11153","repositories_listed":5,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pix2vox-context-aware-3d-reconstruction-from#ran","syntology_url":"https://syntology.ai/paper/1901.11153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.11153"}},"official":{"repos":["hzxie/Pix2Vox"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/attentional-aggregation-of-deep-feature-sets","slug":"attentional-aggregation-of-deep-feature-sets","title":"Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction","date":"2018-08-02","arxiv_id":"1808.00758","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/attentional-aggregation-of-deep-feature-sets#ran","syntology_url":"https://syntology.ai/paper/1808.00758","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.00758"}},"official":{"repos":["Yang7879/AttSets"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"1f08367d193aab22f980a0904deb830f0bdc11a51fe1d463be43bd0a2d48ede0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}