{"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/im2height-height-estimation-from-single","title":"IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network","arxiv_id":"1802.10249","date":"2018-02-28","proceeding":null,"authors":["Lichao Mou","Xiao Xiang Zhu"],"abstract":"In this paper we tackle a very novel problem, namely height estimation from a\nsingle monocular remote sensing image, which is inherently ambiguous, and a\ntechnically ill-posed problem, with a large source of uncertainty coming from\nthe overall scale. We propose a fully convolutional-deconvolutional network\narchitecture being trained end-to-end, encompassing residual learning, to model\nthe ambiguous mapping between monocular remote sensing images and height maps.\nSpecifically, it is composed of two parts, i.e., convolutional sub-network and\ndeconvolutional sub-network. The former corresponds to feature extractor that\ntransforms the input remote sensing image to high-level multidimensional\nfeature representation, whereas the latter plays the role of a height generator\nthat produces height map from the feature extracted from the convolutional\nsub-network. Moreover, to preserve fine edge details of estimated height maps,\nwe introduce a skip connection to the network, which is able to shuttle\nlow-level visual information, e.g., object boundaries and edges, directly\nacross the network. To demonstrate the usefulness of single-view height\nprediction, we show a practical example of instance segmentation of buildings\nusing estimated height map. This paper, for the first time in the remote\nsensing community, attempts to estimate height from monocular vision. The\nproposed network is validated using a large-scale high resolution aerial image\ndata set covered an area of Berlin. Both visual and quantitative analysis of\nthe experimental results demonstrate the effectiveness of our approach.","url_abs":"http://arxiv.org/abs/1802.10249v1","url_pdf":"http://arxiv.org/pdf/1802.10249v1.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":"im2height-height-estimation-from-single","repo_url":"https://github.com/dettmar/im2height","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}