{"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/mvdepthnet-real-time-multiview-depth","title":"MVDepthNet: Real-time Multiview Depth Estimation Neural Network","arxiv_id":"1807.08563","date":"2018-07-23","proceeding":null,"authors":["Kaixuan Wang","Shaojie Shen"],"abstract":"Although deep neural networks have been widely applied to computer vision\nproblems, extending them into multiview depth estimation is non-trivial. In\nthis paper, we present MVDepthNet, a convolutional network to solve the depth\nestimation problem given several image-pose pairs from a localized monocular\ncamera in neighbor viewpoints. Multiview observations are encoded in a cost\nvolume and then combined with the reference image to estimate the depth map\nusing an encoder-decoder network. By encoding the information from multiview\nobservations into the cost volume, our method achieves real-time performance\nand the flexibility of traditional methods that can be applied regardless of\nthe camera intrinsic parameters and the number of images. Geometric data\naugmentation is used to train MVDepthNet. We further apply MVDepthNet in a\nmonocular dense mapping system that continuously estimates depth maps using a\nsingle localized moving camera. Experiments show that our method can generate\ndepth maps efficiently and precisely.","url_abs":"http://arxiv.org/abs/1807.08563v1","url_pdf":"http://arxiv.org/pdf/1807.08563v1.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":"mvdepthnet-real-time-multiview-depth","repo_url":"https://github.com/HKUST-Aerial-Robotics/MVDepthNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"depth-estimation","task_name":"Depth Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.08563","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.08563"}},"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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