{"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/sceneednet-a-deep-learning-approach-for-scene","title":"SceneEDNet: A Deep Learning Approach for Scene Flow Estimation","arxiv_id":"1807.03464","date":"2018-07-10","proceeding":null,"authors":["Ravi Kumar Thakur","Snehasis Mukherjee"],"abstract":"Estimating scene flow in RGB-D videos is attracting much interest of the\ncomputer vision researchers, due to its potential applications in robotics. The\nstate-of-the-art techniques for scene flow estimation, typically rely on the\nknowledge of scene structure of the frame and the correspondence between\nframes. However, with the increasing amount of RGB-D data captured from\nsophisticated sensors like Microsoft Kinect, and the recent advances in the\narea of sophisticated deep learning techniques, introduction of an efficient\ndeep learning technique for scene flow estimation, is becoming important. This\npaper introduces a first effort to apply a deep learning method for direct\nestimation of scene flow by presenting a fully convolutional neural network\nwith an encoder-decoder (ED) architecture. The proposed network SceneEDNet\ninvolves estimation of three dimensional motion vectors of all the scene points\nfrom sequence of stereo images. The training for direct estimation of scene\nflow is done using consecutive pairs of stereo images and corresponding scene\nflow ground truth. The proposed architecture is applied on a huge dataset and\nprovides meaningful results.","url_abs":"http://arxiv.org/abs/1807.03464v1","url_pdf":"http://arxiv.org/pdf/1807.03464v1.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":"sceneednet-a-deep-learning-approach-for-scene","repo_url":"https://github.com/ravikt/sceneednet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"scene-flow-estimation","task_name":"Scene Flow Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.03464","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}