{"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/efficient-multi-view-stereo-by-iterative","title":"Efficient Multi-View Stereo by Iterative Dynamic Cost Volume","arxiv_id":null,"date":"2022-01-01","proceeding":"CVPR 2022 1","authors":["Shaoqian Wang","Bo Li","Yuchao Dai"],"abstract":"    In this paper, we propose a novel iterative dynamic cost volume for multi-view stereo. Compared with other works, our cost volume is much lighter, thus could be processed with 2D convolution based GRU. Notably, the every-step output of the GRU could be further used to generate new cost volume. In this way, an iterative GRU-based optimizer is constructed. Furthermore, we present a cascade and hierarchical refinement architecture to utilize the multi-scale information and speed up the convergence. Specifically, a lightweight 3D CNN is utilized to generate the coarsest initial depth map which is essential to launch the GRU and guarantee a fast convergence. Then the depth map is refined by multi-stage GRUs which work on the pyramid feature maps. Extensive experiments on DTU and Tanks & Temples benchmarks demonstrate that our method could achieve state-of-the-art results in terms of accuracy, speed and memory usage. Code will be released at https://github.com/bdwsq1996/Effi-MVS.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Efficient_Multi-View_Stereo_by_Iterative_Dynamic_Cost_Volume_CVPR_2022_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Efficient_Multi-View_Stereo_by_Iterative_Dynamic_Cost_Volume_CVPR_2022_paper.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":"efficient-multi-view-stereo-by-iterative","repo_url":"https://github.com/bdwsq1996/effi-mvs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"3d-cnn","method_name":"3D CNN"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"gru","method_name":"GRU"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}