{"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/unsupervised-cnn-for-single-view-depth","title":"Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue","arxiv_id":"1603.04992","date":"2016-03-16","proceeding":null,"authors":["Ravi Garg","Vijay Kumar BG","Gustavo Carneiro","Ian Reid"],"abstract":"A significant weakness of most current deep Convolutional Neural Networks is\nthe need to train them using vast amounts of manu- ally labelled data. In this\nwork we propose a unsupervised framework to learn a deep convolutional neural\nnetwork for single view depth predic- tion, without requiring a pre-training\nstage or annotated ground truth depths. We achieve this by training the network\nin a manner analogous to an autoencoder. At training time we consider a pair of\nimages, source and target, with small, known camera motion between the two such\nas a stereo pair. We train the convolutional encoder for the task of predicting\nthe depth map for the source image. To do so, we explicitly generate an inverse\nwarp of the target image using the predicted depth and known inter-view\ndisplacement, to reconstruct the source image; the photomet- ric error in the\nreconstruction is the reconstruction loss for the encoder. The acquisition of\nthis training data is considerably simpler than for equivalent systems,\nrequiring no manual annotation, nor calibration of depth sensor to camera. We\nshow that our network trained on less than half of the KITTI dataset (without\nany further augmentation) gives com- parable performance to that of the state\nof art supervised methods for single view depth estimation.","url_abs":"http://arxiv.org/abs/1603.04992v2","url_pdf":"http://arxiv.org/pdf/1603.04992v2.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":"unsupervised-cnn-for-single-view-depth","repo_url":"https://github.com/Ravi-Garg/Unsupervised_Depth_Estimation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"unsupervised-cnn-for-single-view-depth","repo_url":"https://github.com/zhangzheyu13/SingleViewDepth","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.04992","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1603.04992"}},"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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