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Recently, several methods reframe the MDE as a classification-regression problem where a linear combination of probabilistic distribution and bin centers is used to predict depth. In this paper, we propose a novel concept of iterative elastic bins (IEBins) for the classification-regression-based MDE. The proposed IEBins aims to search for high-quality depth by progressively optimizing the search range, which involves multiple stages and each stage performs a finer-grained depth search in the target bin on top of its previous stage. To alleviate the possible error accumulation during the iterative process, we utilize a novel elastic target bin to replace the original target bin, the width of which is adjusted elastically based on the depth uncertainty. Furthermore, we develop a dedicated framework composed of a feature extractor and an iterative optimizer that has powerful temporal context modeling capabilities benefiting from the GRU-based architecture. Extensive experiments on the KITTI, NYU-Depth-v2 and SUN RGB-D datasets demonstrate that the proposed method surpasses prior state-of-the-art competitors. The source code is publicly available at https://github.com/ShuweiShao/IEBins.","url_abs":"https://arxiv.org/abs/2309.14137v1","url_pdf":"https://arxiv.org/pdf/2309.14137v1.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":"iebins-iterative-elastic-bins-for-monocular-1","repo_url":"https://github.com/shuweishao/iebins","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/monocular-depth-estimation-on-kitti-eigen","task":"Monocular Depth Estimation","dataset":"KITTI Eigen split","model":"IEBins","rank_in_archive_order":18,"of":79,"metrics":{"Delta < 1.25":"0.978","Delta < 1.25^2":"0.998","Delta < 1.25^3":"0.999","RMSE":"2.011","RMSE log":"0.075","Sq Rel":"0.142","absolute relative error":"0.050"},"uses_additional_data":false},{"leaderboard":"/sota/monocular-depth-estimation-on-nyu-depth-v2","task":"Monocular Depth Estimation","dataset":"NYU-Depth V2","model":"IEBins","rank_in_archive_order":35,"of":85,"metrics":{"Delta < 1.25":"0.936","Delta < 1.25^2":"0.992","Delta < 1.25^3":"0.998","RMSE":"0.314","absolute relative error":"0.087","log 10":"0.038"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2309.14137","atlas_url":"https://app.syntology.ai/?focus=2309.14137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14137"}},"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. 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