{"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/dpsnet-end-to-end-deep-plane-sweep-stereo-1","title":"DPSNet: End-to-end Deep Plane Sweep Stereo","arxiv_id":"1905.00538","date":"2019-05-02","proceeding":"ICLR 2019 5","authors":["Sunghoon Im","Hae-Gon Jeon","Stephen Lin","In So Kweon"],"abstract":"Multiview stereo aims to reconstruct scene depth from images acquired by a\ncamera under arbitrary motion. Recent methods address this problem through deep\nlearning, which can utilize semantic cues to deal with challenges such as\ntextureless and reflective regions. In this paper, we present a convolutional\nneural network called DPSNet (Deep Plane Sweep Network) whose design is\ninspired by best practices of traditional geometry-based approaches for dense\ndepth reconstruction. Rather than directly estimating depth and/or optical flow\ncorrespondence from image pairs as done in many previous deep learning methods,\nDPSNet takes a plane sweep approach that involves building a cost volume from\ndeep features using the plane sweep algorithm, regularizing the cost volume via\na context-aware cost aggregation, and regressing the dense depth map from the\ncost volume. The cost volume is constructed using a differentiable warping\nprocess that allows for end-to-end training of the network. Through the\neffective incorporation of conventional multiview stereo concepts within a deep\nlearning framework, DPSNet achieves state-of-the-art reconstruction results on\na variety of challenging datasets.","url_abs":"http://arxiv.org/abs/1905.00538v1","url_pdf":"http://arxiv.org/pdf/1905.00538v1.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":"dpsnet-end-to-end-deep-plane-sweep-stereo-1","repo_url":"https://github.com/sunghoonim/DPSNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1905.00538","atlas_url":"https://app.syntology.ai/?focus=1905.00538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.00538"}},"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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