{"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/monocular-depth-estimation-using-multi-scale","title":"Monocular Depth Estimation using Multi-Scale Continuous CRFs as Sequential Deep Networks","arxiv_id":"1803.00891","date":"2018-03-01","proceeding":null,"authors":["Dan Xu","Elisa Ricci","Wanli Ouyang","Xiaogang Wang","Nicu Sebe"],"abstract":"Depth cues have been proved very useful in various computer vision and\nrobotic tasks. This paper addresses the problem of monocular depth estimation\nfrom a single still image. Inspired by the effectiveness of recent works on\nmulti-scale convolutional neural networks (CNN), we propose a deep model which\nfuses complementary information derived from multiple CNN side outputs.\nDifferent from previous methods using concatenation or weighted average\nschemes, the integration is obtained by means of continuous Conditional Random\nFields (CRFs). In particular, we propose two different variations, one based on\na cascade of multiple CRFs, the other on a unified graphical model. By\ndesigning a novel CNN implementation of mean-field updates for continuous CRFs,\nwe show that both proposed models can be regarded as sequential deep networks\nand that training can be performed end-to-end. Through an extensive\nexperimental evaluation, we demonstrate the effectiveness of the proposed\napproach and establish new state of the art results for the monocular depth\nestimation task on three publicly available datasets, i.e. NYUD-V2, Make3D and\nKITTI.","url_abs":"http://arxiv.org/abs/1803.00891v1","url_pdf":"http://arxiv.org/pdf/1803.00891v1.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":"monocular-depth-estimation-using-multi-scale","repo_url":"https://github.com/danxuhk/ContinuousCRF-CNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.00891","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}