{"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/3d-classification/papers/ran/1","list_of":"/task/3d-classification","task":"3D Classification","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":[1,12],"of":12,"counts":{"archive_papers_tagged":79,"with_a_code_link":42,"where_syntology_ran_a_sample":12,"not_listed_spam_title":0,"listed":79,"listed_where_code_ran":12,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":8,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":8,"listed_every_run_a_failure_of_syntologys_instrument":4,"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/3d-classification/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/critical-points-an-agile-point-cloud","slug":"critical-points-an-agile-point-cloud","title":"Robustifying Point Cloud Networks by Refocusing","date":"2023-08-10","arxiv_id":"2308.05525","repositories_listed":2,"syntology":{"n":22,"n_ran":16,"n_constructed":0,"n_ran_checked":14,"n_instrument":2,"n_unverified":6,"n_honours":2,"n_violates":1,"n_no_contract":11,"n_pointer_only":22,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 2 honoured, 1 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/critical-points-an-agile-point-cloud#ran","syntology_url":"https://syntology.ai/paper/2308.05525","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.05525"}},"official":{"repos":["yossilevii100/critical_points2","yossilevii100/refocusing"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/openshape-scaling-up-3d-shape-representation-1","slug":"openshape-scaling-up-3d-shape-representation-1","title":"OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding","date":"2023-05-18","arxiv_id":"2305.10764","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"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) · 4 unverified","sample_list":"/paper/openshape-scaling-up-3d-shape-representation-1#ran","syntology_url":"https://syntology.ai/paper/2305.10764","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10764"}},"official":null}},{"url":"/paper/ulip-2-towards-scalable-multimodal-pre","slug":"ulip-2-towards-scalable-multimodal-pre","title":"ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding","date":"2023-05-14","arxiv_id":"2305.08275","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ulip-2-towards-scalable-multimodal-pre#ran","syntology_url":"https://syntology.ai/paper/2305.08275","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.08275"}},"official":{"repos":["salesforce/ulip"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ulip-learning-unified-representation-of","slug":"ulip-learning-unified-representation-of","title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","date":"2022-12-10","arxiv_id":"2212.05171","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ulip-learning-unified-representation-of#ran","syntology_url":"https://syntology.ai/paper/2212.05171","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.05171"}},"official":{"repos":["salesforce/ulip"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pointclip-v2-adapting-clip-for-powerful-3d","slug":"pointclip-v2-adapting-clip-for-powerful-3d","title":"PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning","date":"2022-11-21","arxiv_id":"2211.11682","repositories_listed":2,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":4,"phrase":"8 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; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/pointclip-v2-adapting-clip-for-powerful-3d#ran","syntology_url":"https://syntology.ai/paper/2211.11682","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.11682"}},"official":{"repos":["yangyangyang127/pointclip_v2"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/pointnext-revisiting-pointnet-with-improved","slug":"pointnext-revisiting-pointnet-with-improved","title":"PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies","date":"2022-06-09","arxiv_id":"2206.04670","repositories_listed":3,"syntology":{"n":17,"n_ran":10,"n_constructed":0,"n_ran_checked":4,"n_instrument":6,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 6 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/pointnext-revisiting-pointnet-with-improved#ran","syntology_url":"https://syntology.ai/paper/2206.04670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.04670"}},"official":{"repos":["guochengqian/pointnext"],"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":["community","listed","official"]}}},{"url":"/paper/app-net-auxiliary-point-based-push-and-pull","slug":"app-net-auxiliary-point-based-push-and-pull","title":"APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classification","date":"2022-05-02","arxiv_id":"2205.00847","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/app-net-auxiliary-point-based-push-and-pull#ran","syntology_url":"https://syntology.ai/paper/2205.00847","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.00847"}},"official":{"repos":["mcg-nju/app-net"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/voint-cloud-multi-view-point-cloud","slug":"voint-cloud-multi-view-point-cloud","title":"Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding","date":"2021-11-30","arxiv_id":"2111.15363","repositories_listed":2,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":4,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/voint-cloud-multi-view-point-cloud#ran","syntology_url":"https://syntology.ai/paper/2111.15363","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.15363"}},"official":{"repos":["ajhamdi/vointcloud"],"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/multimodal-semi-supervised-learning-for3d","slug":"multimodal-semi-supervised-learning-for3d","title":"Multimodal Semi-Supervised Learning for 3D Objects","date":"2021-10-22","arxiv_id":"2110.11601","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multimodal-semi-supervised-learning-for3d#ran","syntology_url":"https://syntology.ai/paper/2110.11601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.11601"}},"official":{"repos":["AutoAILab/M2CP-Learning"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/subdivision-based-mesh-convolution-networks","slug":"subdivision-based-mesh-convolution-networks","title":"Subdivision-Based Mesh Convolution Networks","date":"2021-06-04","arxiv_id":"2106.02285","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/subdivision-based-mesh-convolution-networks#ran","syntology_url":"https://syntology.ai/paper/2106.02285","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02285"}},"official":{"repos":["lzhengning/SubdivNet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mvtn-multi-view-transformation-network-for-3d","slug":"mvtn-multi-view-transformation-network-for-3d","title":"MVTN: Multi-View Transformation Network for 3D Shape Recognition","date":"2020-11-26","arxiv_id":"2011.13244","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mvtn-multi-view-transformation-network-for-3d#ran","syntology_url":"https://syntology.ai/paper/2011.13244","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.13244"}},"official":{"repos":["ajhamdi/MVTN"],"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"]}}},{"url":"/paper/learning-so3-equivariant-representations-with","slug":"learning-so3-equivariant-representations-with","title":"Learning SO(3) Equivariant Representations with Spherical CNNs","date":"2017-11-17","arxiv_id":"1711.06721","repositories_listed":3,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-so3-equivariant-representations-with#ran","syntology_url":"https://syntology.ai/paper/1711.06721","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.06721"}},"official":{"repos":["daniilidis-group/spherical-cnn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}}],"record_sha256":"cf37c1e0175e7e589204735d57b88780732d76f44d4f45b2f75471b5319d562f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}