{"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/robust-3d-semantic-segmentation/papers/ran/1","list_of":"/task/robust-3d-semantic-segmentation","task":"Robust 3D Semantic Segmentation","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":[1,9],"of":9,"counts":{"archive_papers_tagged":19,"with_a_code_link":17,"where_syntology_ran_a_sample":9,"not_listed_spam_title":0,"listed":19,"listed_where_code_ran":9,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":8,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":8,"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/robust-3d-semantic-segmentation/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/using-a-waffle-iron-for-automotive-point","slug":"using-a-waffle-iron-for-automotive-point","title":"Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation","date":"2023-01-24","arxiv_id":"2301.10100","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":5,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 ran (of which 5 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; every one of the 5 samples that ran constructed an object rather than computing a result","sample_list":"/paper/using-a-waffle-iron-for-automotive-point#ran","syntology_url":"https://syntology.ai/paper/2301.10100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.10100"}},"official":{"repos":["valeoai/waffleiron"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cpgnet-cascade-point-grid-fusion-network-for","slug":"cpgnet-cascade-point-grid-fusion-network-for","title":"CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation","date":"2022-04-21","arxiv_id":"2204.09914","repositories_listed":3,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cpgnet-cascade-point-grid-fusion-network-for#ran","syntology_url":"https://syntology.ai/paper/2204.09914","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.09914"}},"official":{"repos":["GangZhang842/CPGNet"],"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/searching-efficient-3d-architectures-with","slug":"searching-efficient-3d-architectures-with","title":"Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution","date":"2020-07-31","arxiv_id":"2007.16100","repositories_listed":6,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/searching-efficient-3d-architectures-with#ran","syntology_url":"https://syntology.ai/paper/2007.16100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.16100"}},"official":{"repos":["mit-han-lab/spvnas"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/polarnet-an-improved-grid-representation-for","slug":"polarnet-an-improved-grid-representation-for","title":"PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation","date":"2020-03-31","arxiv_id":"2003.14032","repositories_listed":4,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":3,"n_no_contract":5,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 3 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/polarnet-an-improved-grid-representation-for#ran","syntology_url":"https://syntology.ai/paper/2003.14032","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.14032"}},"official":{"repos":["edwardzhou130/PolarSeg"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"url":"/paper/salsanext-fast-semantic-segmentation-of-lidar","slug":"salsanext-fast-semantic-segmentation-of-lidar","title":"SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving","date":"2020-03-07","arxiv_id":"2003.03653","repositories_listed":5,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"8 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; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/salsanext-fast-semantic-segmentation-of-lidar#ran","syntology_url":"https://syntology.ai/paper/2003.03653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.03653"}},"official":{"repos":["TiagoCortinhal/SalsaNext"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official","unlocated"]}}},{"url":"/paper/4d-spatio-temporal-convnets-minkowski","slug":"4d-spatio-temporal-convnets-minkowski","title":"4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks","date":"2019-04-18","arxiv_id":"1904.08755","repositories_listed":8,"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/4d-spatio-temporal-convnets-minkowski#ran","syntology_url":"https://syntology.ai/paper/1904.08755","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.08755"}},"official":{"repos":["StanfordVL/MinkowskiEngine"],"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/kpconv-flexible-and-deformable-convolution","slug":"kpconv-flexible-and-deformable-convolution","title":"KPConv: Flexible and Deformable Convolution for Point Clouds","date":"2019-04-18","arxiv_id":"1904.08889","repositories_listed":10,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/kpconv-flexible-and-deformable-convolution#ran","syntology_url":"https://syntology.ai/paper/1904.08889","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.08889"}},"official":{"repos":["HuguesTHOMAS/KPConv"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/squeezesegv2-improved-model-structure-and","slug":"squeezesegv2-improved-model-structure-and","title":"SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud","date":"2018-09-22","arxiv_id":"1809.08495","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/squeezesegv2-improved-model-structure-and#ran","syntology_url":"https://syntology.ai/paper/1809.08495","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.08495"}},"official":null}},{"url":"/paper/squeezeseg-convolutional-neural-nets-with","slug":"squeezeseg-convolutional-neural-nets-with","title":"SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud","date":"2017-10-19","arxiv_id":"1710.07368","repositories_listed":5,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/squeezeseg-convolutional-neural-nets-with#ran","syntology_url":"https://syntology.ai/paper/1710.07368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.07368"}},"official":null}}],"record_sha256":"6d93a1f6bf7c4fcecb9ca65c8ddcf344147a36a4145cf2b6e51618dda9a809b0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}