{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/paper/meteornet-deep-learning-on-dynamic-3d-point","title":"MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences","arxiv_id":"1910.09165","date":"2019-10-21","proceeding":"ICCV 2019 10","authors":["Xingyu Liu","Mengyuan Yan","Jeannette Bohg"],"abstract":"Understanding dynamic 3D environment is crucial for robotic agents and many other applications. We propose a novel neural network architecture called $MeteorNet$ for learning representations for dynamic 3D point cloud sequences. Different from previous work that adopts a grid-based representation and applies 3D or 4D convolutions, our network directly processes point clouds. We propose two ways to construct spatiotemporal neighborhoods for each point in the point cloud sequence. Information from these neighborhoods is aggregated to learn features per point. We benchmark our network on a variety of 3D recognition tasks including action recognition, semantic segmentation and scene flow estimation. MeteorNet shows stronger performance than previous grid-based methods while achieving state-of-the-art performance on Synthia. MeteorNet also outperforms previous baseline methods that are able to process at most two consecutive point clouds. To the best of our knowledge, this is the first work on deep learning for dynamic raw point cloud sequences.","url_abs":"https://arxiv.org/abs/1910.09165v2","url_pdf":"https://arxiv.org/pdf/1910.09165v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"meteornet-deep-learning-on-dynamic-3d-point","repo_url":"https://github.com/xingyul/meteornet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"meteornet-deep-learning-on-dynamic-3d-point","repo_url":"https://github.com/jx-zhong-for-academic-purpose/kinet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"scene-flow-estimation","task_name":"Scene Flow Estimation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1910.09165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.09165"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jx-zhong-for-academic-purpose/kinet","reach":{"status":"ok","spdx":"NOASSERTION"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/xingyul/meteornet","reach":null}],"summary":{"ran_fixture":3},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"c7faaa89d45c06a0","entry":"depth_pop_up_v2","repo":"xingyul/meteornet","repo_kind":"official","path":"scene_flow_kitti/gen_kitti_flow.py","file_url":"https://github.com/xingyul/meteornet/blob/HEAD/scene_flow_kitti/gen_kitti_flow.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c7faaa89d45c06a0"}},{"code_sha256_prefix":"e743281f3cfff96b","entry":"project","repo":"xingyul/meteornet","repo_kind":"official","path":"scene_flow_kitti/gen_kitti_flow.py","file_url":"https://github.com/xingyul/meteornet/blob/HEAD/scene_flow_kitti/gen_kitti_flow.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e743281f3cfff96b"}},{"code_sha256_prefix":"3968ab0292a2f677","entry":"transform","repo":"xingyul/meteornet","repo_kind":"official","path":"scene_flow_kitti/gen_kitti_flow.py","file_url":"https://github.com/xingyul/meteornet/blob/HEAD/scene_flow_kitti/gen_kitti_flow.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3968ab0292a2f677"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}