{"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/deep-stereo-using-adaptive-thin-volume","title":"Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness","arxiv_id":"1911.12012","date":"2019-11-27","proceeding":"CVPR 2020 6","authors":["Shuo Cheng","Zexiang Xu","Shilin Zhu","Zhuwen Li","Li Erran Li","Ravi Ramamoorthi","Hao Su"],"abstract":"We present Uncertainty-aware Cascaded Stereo Network (UCS-Net) for 3D reconstruction from multiple RGB images. Multi-view stereo (MVS) aims to reconstruct fine-grained scene geometry from multi-view images. Previous learning-based MVS methods estimate per-view depth using plane sweep volumes with a fixed depth hypothesis at each plane; this generally requires densely sampled planes for desired accuracy, and it is very hard to achieve high-resolution depth. In contrast, we propose adaptive thin volumes (ATVs); in an ATV, the depth hypothesis of each plane is spatially varying, which adapts to the uncertainties of previous per-pixel depth predictions. Our UCS-Net has three stages: the first stage processes a small standard plane sweep volume to predict low-resolution depth; two ATVs are then used in the following stages to refine the depth with higher resolution and higher accuracy. Our ATV consists of only a small number of planes; yet, it efficiently partitions local depth ranges within learned small intervals. In particular, we propose to use variance-based uncertainty estimates to adaptively construct ATVs; this differentiable process introduces reasonable and fine-grained spatial partitioning. Our multi-stage framework progressively subdivides the vast scene space with increasing depth resolution and precision, which enables scene reconstruction with high completeness and accuracy in a coarse-to-fine fashion. We demonstrate that our method achieves superior performance compared with state-of-the-art benchmarks on various challenging datasets.","url_abs":"https://arxiv.org/abs/1911.12012v2","url_pdf":"https://arxiv.org/pdf/1911.12012v2.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":"deep-stereo-using-adaptive-thin-volume","repo_url":"https://github.com/touristCheng/UCSNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-reconstruction","task_name":"3D Reconstruction"},{"task_slug":"point-clouds","task_name":"Point Clouds"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-reconstruction-on-dtu","task":"3D Reconstruction","dataset":"DTU","model":"UCSNet","rank_in_archive_order":14,"of":24,"metrics":{"Acc":"0.338","Comp":"0.349","Overall":"0.344"},"uses_additional_data":false},{"leaderboard":"/sota/point-clouds-on-tanks-and-temples","task":"Point Clouds","dataset":"Tanks and Temples","model":"UCSNet","rank_in_archive_order":19,"of":21,"metrics":{"Mean F1 (Intermediate)":"54.83"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1911.12012","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.12012"}},"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. 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