{"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/hi-slam2-geometry-aware-gaussian-slam-for","title":"HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction","arxiv_id":"2411.17982","date":"2024-11-27","proceeding":null,"authors":["Wei zhang","Qing Cheng","David Skuddis","Niclas Zeller","Daniel Cremers","Norbert Haala"],"abstract":"We present HI-SLAM2, a geometry-aware Gaussian SLAM system that achieves fast and accurate monocular scene reconstruction using only RGB input. Existing Neural SLAM or 3DGS-based SLAM methods often trade off between rendering quality and geometry accuracy, our research demonstrates that both can be achieved simultaneously with RGB input alone. The key idea of our approach is to enhance the ability for geometry estimation by combining easy-to-obtain monocular priors with learning-based dense SLAM, and then using 3D Gaussian splatting as our core map representation to efficiently model the scene. Upon loop closure, our method ensures on-the-fly global consistency through efficient pose graph bundle adjustment and instant map updates by explicitly deforming the 3D Gaussian units based on anchored keyframe updates. Furthermore, we introduce a grid-based scale alignment strategy to maintain improved scale consistency in prior depths for finer depth details. Through extensive experiments on Replica, ScanNet, and ScanNet++, we demonstrate significant improvements over existing Neural SLAM methods and even surpass RGB-D-based methods in both reconstruction and rendering quality. The project page and source code will be made available at https://hi-slam2.github.io/.","url_abs":"https://arxiv.org/abs/2411.17982v2","url_pdf":"https://arxiv.org/pdf/2411.17982v2.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":"hi-slam2-geometry-aware-gaussian-slam-for","repo_url":"https://github.com/Willyzw/HI-SLAM2","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"3dgs","task_name":"3DGS"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2411.17982","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.17982"}},"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/Willyzw/HI-SLAM2","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"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":"0ff7743e516d8df8","entry":"cvx_upsample","repo":"Willyzw/HI-SLAM2","repo_kind":"official","path":"hislam2/modules/droid_net.py","file_url":"https://github.com/Willyzw/HI-SLAM2/blob/HEAD/hislam2/modules/droid_net.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"0ff7743e516d8df8"}},{"code_sha256_prefix":"d6387df7878233e0","entry":"to_se3_matrix","repo":"Willyzw/HI-SLAM2","repo_kind":"official","path":"tsdf_integrate.py","file_url":"https://github.com/Willyzw/HI-SLAM2/blob/HEAD/tsdf_integrate.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"d6387df7878233e0"}},{"code_sha256_prefix":"81146f41f7c6c28d","entry":"upsample_disp","repo":"Willyzw/HI-SLAM2","repo_kind":"official","path":"hislam2/modules/droid_net.py","file_url":"https://github.com/Willyzw/HI-SLAM2/blob/HEAD/hislam2/modules/droid_net.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"81146f41f7c6c28d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}