{"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/geonet-geometric-neural-network-for-joint","title":"GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation","arxiv_id":null,"date":"2018-06-01","proceeding":"CVPR 2018 6","authors":["Xiaojuan Qi","Renjie Liao","Zhengzhe Liu","Raquel Urtasun","Jiaya Jia"],"abstract":"In this paper, we propose Geometric Neural Network (GeoNet) to jointly predict depth and surface normal maps from a single image. Building on top of two-stream CNNs, our GeoNet incorporates geometric relation between depth and surface normal via the new depth-to-normal and normal- to-depth networks. Depth-to-normal network exploits the least square solution of surface normal from depth and im- proves its quality with a residual module. Normal-to-depth network, contrarily, refines the depth map based on the con- straints from the surface normal through a kernel regression module, which has no parameter to learn. These two net- works enforce the underlying model to efficiently predict depth and surface normal for high consistency and corre- sponding accuracy. Our experiments on NYU v2 dataset verify that our GeoNet is able to predict geometrically con- sistent depth and normal maps. It achieves top performance on surface normal estimation and is on par with state-of-the- art depth estimation methods.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2018/html/Qi_GeoNet_Geometric_Neural_CVPR_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2018/papers/Qi_GeoNet_Geometric_Neural_CVPR_2018_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":"geonet-geometric-neural-network-for-joint","repo_url":"https://github.com/xjqi/GeoNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"surface-normal-estimation","task_name":"Surface Normal Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}