{"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/street-gaussians-for-modeling-dynamic-urban","title":"Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting","arxiv_id":"2401.01339","date":"2024-01-02","proceeding":null,"authors":["Yunzhi Yan","Haotong Lin","Chenxu Zhou","Weijie Wang","Haiyang Sun","Kun Zhan","Xianpeng Lang","Xiaowei Zhou","Sida Peng"],"abstract":"This paper aims to tackle the problem of modeling dynamic urban streets for autonomous driving scenes. Recent methods extend NeRF by incorporating tracked vehicle poses to animate vehicles, enabling photo-realistic view synthesis of dynamic urban street scenes. However, significant limitations are their slow training and rendering speed. We introduce Street Gaussians, a new explicit scene representation that tackles these limitations. Specifically, the dynamic urban scene is represented as a set of point clouds equipped with semantic logits and 3D Gaussians, each associated with either a foreground vehicle or the background. To model the dynamics of foreground object vehicles, each object point cloud is optimized with optimizable tracked poses, along with a 4D spherical harmonics model for the dynamic appearance. The explicit representation allows easy composition of object vehicles and background, which in turn allows for scene editing operations and rendering at 135 FPS (1066 $\\times$ 1600 resolution) within half an hour of training. The proposed method is evaluated on multiple challenging benchmarks, including KITTI and Waymo Open datasets. Experiments show that the proposed method consistently outperforms state-of-the-art methods across all datasets. The code will be released to ensure reproducibility.","url_abs":"https://arxiv.org/abs/2401.01339v3","url_pdf":"https://arxiv.org/pdf/2401.01339v3.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":"street-gaussians-for-modeling-dynamic-urban","repo_url":"https://github.com/zju3dv/street_gaussians","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"street-gaussians-for-modeling-dynamic-urban","repo_url":"https://github.com/lightwheelai/street-gaussians-ns","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"nerf","task_name":"NeRF"},{"task_slug":"object","task_name":"Object"}],"methods":[{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2401.01339","atlas_url":"https://app.syntology.ai/?focus=2401.01339","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.01339"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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/lightwheelai/street-gaussians-ns","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/zju3dv/street_gaussians","reach":{"status":"ok","spdx":"NOASSERTION"}}],"summary":{"ran":3,"ran_honours":3},"by_repo_kind":{"official":{"samples":2,"ran":2,"repositories":1},"listed":{"samples":4,"ran":4,"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":2,"samples":[{"code_sha256_prefix":"1a536a8480df2817","entry":"IDFT","repo":"lightwheelai/street-gaussians-ns","repo_kind":"listed","path":"street_gaussians_ns/sgn_splatfacto_scene_graph.py","file_url":"https://github.com/lightwheelai/street-gaussians-ns/blob/HEAD/street_gaussians_ns/sgn_splatfacto_scene_graph.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"1a536a8480df2817"}},{"code_sha256_prefix":"3d2c1a432b1a3561","entry":"RGB2SH","repo":"lightwheelai/street-gaussians-ns","repo_kind":"listed","path":"street_gaussians_ns/sgn_splatfacto.py","file_url":"https://github.com/lightwheelai/street-gaussians-ns/blob/HEAD/street_gaussians_ns/sgn_splatfacto.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"3d2c1a432b1a3561"}},{"code_sha256_prefix":"508a98451f5b78fe","entry":"SH2RGB","repo":"lightwheelai/street-gaussians-ns","repo_kind":"listed","path":"street_gaussians_ns/sgn_splatfacto.py","file_url":"https://github.com/lightwheelai/street-gaussians-ns/blob/HEAD/street_gaussians_ns/sgn_splatfacto.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"508a98451f5b78fe"}},{"code_sha256_prefix":"f9a78cc774a03dfe","entry":"get_PCA_Norm","repo":"zju3dv/street_gaussians","repo_kind":"official","path":"lib/datasets/base_readers.py","file_url":"https://github.com/zju3dv/street_gaussians/blob/HEAD/lib/datasets/base_readers.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"f9a78cc774a03dfe"}},{"code_sha256_prefix":"a4a058e513f53050","entry":"get_Sphere_Norm","repo":"zju3dv/street_gaussians","repo_kind":"official","path":"lib/datasets/base_readers.py","file_url":"https://github.com/zju3dv/street_gaussians/blob/HEAD/lib/datasets/base_readers.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"a4a058e513f53050"}},{"code_sha256_prefix":"57acaa6d628ba9f8","entry":"random_quat_tensor","repo":"lightwheelai/street-gaussians-ns","repo_kind":"listed","path":"street_gaussians_ns/sgn_splatfacto.py","file_url":"https://github.com/lightwheelai/street-gaussians-ns/blob/HEAD/street_gaussians_ns/sgn_splatfacto.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"57acaa6d628ba9f8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}