{"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/mvs2d-efficient-multi-view-stereo-via","title":"MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutions","arxiv_id":"2104.13325","date":"2021-04-27","proceeding":"CVPR 2022 1","authors":["Zhenpei Yang","Zhile Ren","Qi Shan","QiXing Huang"],"abstract":"Deep learning has made significant impacts on multi-view stereo systems. State-of-the-art approaches typically involve building a cost volume, followed by multiple 3D convolution operations to recover the input image's pixel-wise depth. 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