{"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/learning-depth-from-monocular-videos-using","title":"Learning Depth from Monocular Videos using Direct Methods","arxiv_id":"1712.00175","date":"2017-12-01","proceeding":"CVPR 2018 6","authors":["Chaoyang Wang","Jose Miguel Buenaposada","Rui Zhu","Simon Lucey"],"abstract":"The ability to predict depth from a single image - using recent advances in\nCNNs - is of increasing interest to the vision community. Unsupervised\nstrategies to learning are particularly appealing as they can utilize much\nlarger and varied monocular video datasets during learning without the need for\nground truth depth or stereo. In previous works, separate pose and depth CNN\npredictors had to be determined such that their joint outputs minimized the\nphotometric error. Inspired by recent advances in direct visual odometry (DVO),\nwe argue that the depth CNN predictor can be learned without a pose CNN\npredictor. Further, we demonstrate empirically that incorporation of a\ndifferentiable implementation of DVO, along with a novel depth normalization\nstrategy - substantially improves performance over state of the art that use\nmonocular videos for training.","url_abs":"http://arxiv.org/abs/1712.00175v1","url_pdf":"http://arxiv.org/pdf/1712.00175v1.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":"learning-depth-from-monocular-videos-using","repo_url":"https://github.com/MightyChaos/LKVOLearner","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-and-camera-motion","task_name":"Depth And Camera Motion"},{"task_slug":"visual-odometry","task_name":"Visual Odometry"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.00175","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.00175"}},"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/MightyChaos/LKVOLearner","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"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":"a7b795a964b2396b","entry":"read_text_lines","repo":"MightyChaos/LKVOLearner","repo_kind":"official","path":"src/testKITTI.py","file_url":"https://github.com/MightyChaos/LKVOLearner/blob/HEAD/src/testKITTI.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"a7b795a964b2396b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}