{"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/point-cloud-generation/papers/2","list_of":"/task/point-cloud-generation","task":"Point Cloud Generation","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,117],"of":117,"counts":{"archive_papers_tagged":117,"with_a_code_link":59,"where_syntology_ran_a_sample":20,"not_listed_spam_title":0,"listed":117,"listed_where_code_ran":20,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":16,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":16,"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/point-cloud-generation","prev":"/task/point-cloud-generation","next":null,"papers":[{"url":null,"slug":"point-cloud-generation-with-continuous","title":"Point Cloud Generation with Continuous Conditioning","date":"2022-02-17","arxiv_id":"2202.08526","repositories_listed":0,"syntology":null},{"url":null,"slug":"generate-point-clouds-with-multiscale-details","title":"Generate Point Clouds with Multiscale Details from Graph-Represented Structures","date":"2021-12-13","arxiv_id":"2112.06433","repositories_listed":0,"syntology":null},{"url":null,"slug":"editvae-unsupervised-part-aware-controllable","title":"EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation","date":"2021-10-13","arxiv_id":"2110.06679","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-reconstruction-through-fusion-of-cross","title":"3D Reconstruction through Fusion of Cross-View Images","date":"2021-06-27","arxiv_id":"2106.14306","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-progressive-point-embeddings-for-3d","title":"Learning Progressive Point Embeddings for 3D Point Cloud Generation","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"se-md-a-single-encoder-multiple-decoder-deep","title":"SE-MD: A Single-encoder multiple-decoder deep network for point cloud generation from 2D images","date":"2021-06-17","arxiv_id":"2106.15325","repositories_listed":0,"syntology":null},{"url":null,"slug":"go-with-the-flows-mixtures-of-normalizing","title":"Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction","date":"2021-06-06","arxiv_id":"2106.03135","repositories_listed":0,"syntology":null},{"url":null,"slug":"rpg-learning-recursive-point-cloud-generation","title":"RPG: Learning Recursive Point Cloud Generation","date":"2021-05-29","arxiv_id":"2105.14322","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-point-cloud-generative-model-based-on","title":"A Point Cloud Generative Model Based on Nonequilibrium Thermodynamics","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"chartpointflow-for-topology-aware-3d-point","title":"ChartPointFlow for Topology-Aware 3D Point Cloud Generation","date":"2020-12-04","arxiv_id":"2012.02346","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-geometry-image-representation-for-3d","title":"Learning geometry-image representation for 3D point cloud generation","date":"2020-11-29","arxiv_id":"2011.14289","repositories_listed":0,"syntology":null},{"url":null,"slug":"minimal-adversarial-examples-for-deep","title":"Minimal Adversarial Examples for Deep Learning on 3D Point Clouds","date":"2020-08-27","arxiv_id":"2008.12066","repositories_listed":0,"syntology":null},{"url":null,"slug":"getting-topology-and-point-cloud-generation","title":"Getting Topology and Point Cloud Generation to Mesh","date":"2019-12-08","arxiv_id":"1912.03787","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-adversarial-model-for-generating-3d","title":"DUAL ADVERSARIAL MODEL FOR GENERATING 3D POINT CLOUD","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cnn-based-synthesis-of-realistic-high","title":"CNN-based synthesis of realistic high-resolution LiDAR data","date":"2019-06-28","arxiv_id":"1907.00787","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-convolutional-decoder-for-point-clouds","title":"A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization","date":"2019-06-27","arxiv_id":"1906.11478","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-navigation-scheme-for-optimal-deep","title":"Adaptive Navigation Scheme for Optimal Deep-Sea Localization Using Multimodal Perception Cues","date":"2019-06-12","arxiv_id":"1906.04888","repositories_listed":0,"syntology":null}],"record_sha256":"9ed88bc846b28030fdba91b6e0b318cccbdd6f25cfe493b30f87945369ad2752","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}