{"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/deepmvs-learning-multi-view-stereopsis","title":"DeepMVS: Learning Multi-view Stereopsis","arxiv_id":"1804.00650","date":"2018-04-02","proceeding":"CVPR 2018 6","authors":["Po-Han Huang","Kevin Matzen","Johannes Kopf","Narendra Ahuja","Jia-Bin Huang"],"abstract":"We present DeepMVS, a deep convolutional neural network (ConvNet) for\nmulti-view stereo reconstruction. Taking an arbitrary number of posed images as\ninput, we first produce a set of plane-sweep volumes and use the proposed\nDeepMVS network to predict high-quality disparity maps. The key contributions\nthat enable these results are (1) supervised pretraining on a photorealistic\nsynthetic dataset, (2) an effective method for aggregating information across a\nset of unordered images, and (3) integrating multi-layer feature activations\nfrom the pre-trained VGG-19 network. We validate the efficacy of DeepMVS using\nthe ETH3D Benchmark. Our results show that DeepMVS compares favorably against\nstate-of-the-art conventional MVS algorithms and other ConvNet based methods,\nparticularly for near-textureless regions and thin structures.","url_abs":"http://arxiv.org/abs/1804.00650v1","url_pdf":"http://arxiv.org/pdf/1804.00650v1.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":"deepmvs-learning-multi-view-stereopsis","repo_url":"https://github.com/phuang17/DeepMVS","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-2-Clause"}}],"tasks":[],"methods":[{"method_slug":"vgg-19","method_name":"VGG-19"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.00650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.00650"}},"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/phuang17/DeepMVS","reach":{"status":"ok","spdx":"BSD-2-Clause"}}],"summary":{"ran_honours":1,"unverified":1},"by_repo_kind":{"official":{"samples":2,"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":"56858e04e6fdb2ff","entry":"read_next_bytes","repo":"phuang17/DeepMVS","repo_kind":"official","path":"python/colmap_helpers_for_bin.py","file_url":"https://github.com/phuang17/DeepMVS/blob/HEAD/python/colmap_helpers_for_bin.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"56858e04e6fdb2ff"}},{"code_sha256_prefix":"2d4ca8b389b96264","entry":"max_disparity_adjust","repo":"phuang17/DeepMVS","repo_kind":"official","path":"python/generate_volume_train.py","file_url":"https://github.com/phuang17/DeepMVS/blob/HEAD/python/generate_volume_train.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"2d4ca8b389b96264"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}