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We use a multiscale convolutional network that is able to adapt\neasily to each task using only small modifications, regressing from the input\nimage to the output map directly. Our method progressively refines predictions\nusing a sequence of scales, and captures many image details without any\nsuperpixels or low-level segmentation. We achieve state-of-the-art performance\non benchmarks for all three tasks.","url_abs":"http://arxiv.org/abs/1411.4734v4","url_pdf":"http://arxiv.org/pdf/1411.4734v4.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":"predicting-depth-surface-normals-and-semantic","repo_url":"https://github.com/pixar0407/depth_map","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"predicting-depth-surface-normals-and-semantic","repo_url":"https://github.com/vinceecws/SegNet_PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"predicting-depth-surface-normals-and-semantic","repo_url":"https://github.com/yhlleo/DeepSegmentor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"predicting-depth-surface-normals-and-semantic","repo_url":"https://github.com/imran3180/depth-map-prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"depth-prediction","task_name":"Depth Prediction"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"},{"task_slug":"superpixels","task_name":"Superpixels"},{"task_slug":"surface-normal-estimation","task_name":"Surface Normal Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/monocular-depth-estimation-on-nyu-depth-v2","task":"Monocular Depth Estimation","dataset":"NYU-Depth V2","model":"Eigen et al.","rank_in_archive_order":85,"of":85,"metrics":{"RMSE":"0.641"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1411.4734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1411.4734"}},"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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