{"url":"/sota/zero-shot-transfer-3d-point-cloud","task":{"name":"Zero-Shot Transfer 3D Point Cloud Classification","url":"/task/zero-shot-transfer-3d-point-cloud","note":null},"dataset":{"name":"ModelNet40","url":"/dataset/modelnet"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":null,"description_from":null,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy (%)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy (%)":"higher"}},"counts":{"rows":16,"rows_with_code":16,"rows_with_paper_page":16,"rows_dated":16,"rows_using_additional_data":16},"rows":[{"rank_in_archive_order":1,"model":"Uni3D","metrics":{"Accuracy (%)":"88.2"},"uses_additional_data":true,"paper_date":"2023-10-10","paper":"/paper/uni3d-exploring-unified-3d-representation-at","paper_url":"https://arxiv.org/abs/2310.06773v1","paper_title":"Uni3D: Exploring Unified 3D Representation at Scale","code":"https://github.com/baaivision/uni3d","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"ViT-Lens","metrics":{"Accuracy (%)":"87.6"},"uses_additional_data":true,"paper_date":"2023-08-20","paper":"/paper/vit-lens-towards-omni-modal-representations","paper_url":"https://arxiv.org/abs/2308.10185v2","paper_title":"ViT-Lens: Initiating Omni-Modal Exploration through 3D Insights","code":"https://github.com/TencentARC/ViT-Lens","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"ReCon++","metrics":{"Accuracy (%)":"87.3"},"uses_additional_data":true,"paper_date":"2024-02-27","paper":"/paper/shapellm-universal-3d-object-understanding","paper_url":"https://arxiv.org/abs/2402.17766v3","paper_title":"ShapeLLM: Universal 3D Object Understanding for Embodied Interaction","code":"https://github.com/qizekun/ShapeLLM","n_code_links":3,"syntology":{"n_ran":9,"n_unverified":8,"n_samples":17,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"MixCon3D-PointBERT","metrics":{"Accuracy (%)":"86.8"},"uses_additional_data":true,"paper_date":"2023-11-03","paper":"/paper/mixcon3d-synergizing-multi-view-and-cross","paper_url":"https://arxiv.org/abs/2311.01734v2","paper_title":"Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training","code":"https://github.com/ucsc-vlaa/mixcon3d","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":3,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":5,"model":"TAMM-PointBERT (+dlign)","metrics":{"Accuracy (%)":"86.2"},"uses_additional_data":true,"paper_date":"2024-04-25","paper":"/paper/opendlign-enhancing-open-world-3d-learning","paper_url":"https://arxiv.org/abs/2404.16538v3","paper_title":"OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images","code":"https://github.com/Yebulabula/OpenDlign","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":0,"n_samples":4,"n_pointer_only_licence":4}},{"rank_in_archive_order":6,"model":"OpenShape-PointBERT (+dlign)","metrics":{"Accuracy (%)":"85.4"},"uses_additional_data":true,"paper_date":"2024-04-25","paper":"/paper/opendlign-enhancing-open-world-3d-learning","paper_url":"https://arxiv.org/abs/2404.16538v3","paper_title":"OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images","code":"https://github.com/Yebulabula/OpenDlign","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":0,"n_samples":4,"n_pointer_only_licence":4}},{"rank_in_archive_order":7,"model":"OpenShape-PointBERT","metrics":{"Accuracy (%)":"85.3"},"uses_additional_data":true,"paper_date":"2023-05-18","paper":"/paper/openshape-scaling-up-3d-shape-representation-1","paper_url":"https://arxiv.org/abs/2305.10764v2","paper_title":"OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding","code":"https://github.com/Colin97/OpenShape_code","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":7,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":8,"model":"OpenShape-SparseConv (+dlign)","metrics":{"Accuracy (%)":"85.0"},"uses_additional_data":true,"paper_date":"2024-04-25","paper":"/paper/opendlign-enhancing-open-world-3d-learning","paper_url":"https://arxiv.org/abs/2404.16538v3","paper_title":"OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images","code":"https://github.com/Yebulabula/OpenDlign","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":0,"n_samples":4,"n_pointer_only_licence":4}},{"rank_in_archive_order":9,"model":"OpenShape-SparseConv","metrics":{"Accuracy (%)":"83.4"},"uses_additional_data":true,"paper_date":"2023-05-18","paper":"/paper/openshape-scaling-up-3d-shape-representation-1","paper_url":"https://arxiv.org/abs/2305.10764v2","paper_title":"OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding","code":"https://github.com/Colin97/OpenShape_code","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":7,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":10,"model":"OpenDlign","metrics":{"Accuracy (%)":"82.6"},"uses_additional_data":true,"paper_date":"2024-04-25","paper":"/paper/opendlign-enhancing-open-world-3d-learning","paper_url":"https://arxiv.org/abs/2404.16538v3","paper_title":"OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images","code":"https://github.com/Yebulabula/OpenDlign","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":0,"n_samples":4,"n_pointer_only_licence":4}},{"rank_in_archive_order":11,"model":"PointCLIP V2","metrics":{"Accuracy (%)":"64.22"},"uses_additional_data":true,"paper_date":"2022-11-21","paper":"/paper/pointclip-v2-adapting-clip-for-powerful-3d","paper_url":"https://arxiv.org/abs/2211.11682v2","paper_title":"PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning","code":"https://github.com/zrrskywalker/pointclip","n_code_links":2,"syntology":{"n_ran":5,"n_unverified":7,"n_samples":12,"n_pointer_only_licence":0}},{"rank_in_archive_order":12,"model":"ReCon","metrics":{"Accuracy (%)":"61.7"},"uses_additional_data":true,"paper_date":"2023-02-05","paper":"/paper/contrast-with-reconstruct-contrastive-3d","paper_url":"https://arxiv.org/abs/2302.02318v2","paper_title":"Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining","code":"https://github.com/qizekun/ReCon","n_code_links":5,"syntology":{"n_ran":2,"n_unverified":3,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":13,"model":"ULIP + PointMLP","metrics":{"Accuracy (%)":"61.5"},"uses_additional_data":true,"paper_date":"2022-12-10","paper":"/paper/ulip-learning-unified-representation-of","paper_url":"https://arxiv.org/abs/2212.05171v4","paper_title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","code":"https://github.com/salesforce/ulip","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":14,"model":"ULIP + PointBERT","metrics":{"Accuracy (%)":"60.4"},"uses_additional_data":true,"paper_date":"2022-12-10","paper":"/paper/ulip-learning-unified-representation-of","paper_url":"https://arxiv.org/abs/2212.05171v4","paper_title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","code":"https://github.com/salesforce/ulip","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":15,"model":"CLIP2Point","metrics":{"Accuracy (%)":"49.38"},"uses_additional_data":true,"paper_date":"2022-10-03","paper":"/paper/clip2point-transfer-clip-to-point-cloud","paper_url":"https://arxiv.org/abs/2210.01055v3","paper_title":"CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training","code":"https://github.com/tyhuang0428/CLIP2Point","n_code_links":1,"syntology":null},{"rank_in_archive_order":16,"model":"PointCLIP","metrics":{"Accuracy (%)":"20.18"},"uses_additional_data":true,"paper_date":"2021-12-04","paper":"/paper/pointclip-point-cloud-understanding-by-clip","paper_url":"https://arxiv.org/abs/2112.02413v1","paper_title":"PointCLIP: Point Cloud Understanding by CLIP","code":"https://github.com/zrrskywalker/pointclip","n_code_links":2,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":13,"rows_with_any_sample_ran":10,"distinct_papers_with_graph_line":8,"distinct_papers_with_any_sample_ran":6,"samples_over_distinct_papers":{"n_ran":22,"n_unverified":29,"n_samples":51,"n_pointer_only_licence":7,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":35,"n_unverified":36,"n_samples":71,"n_pointer_only_licence":19,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}