{"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/complete-3d-scene-parsing-from-an-rgbd-image","title":"Complete 3D Scene Parsing from an RGBD Image","arxiv_id":"1710.09490","date":"2017-10-25","proceeding":null,"authors":["Chuhang Zou","Ruiqi Guo","Zhizhong Li","Derek Hoiem"],"abstract":"One major goal of vision is to infer physical models of objects, surfaces,\nand their layout from sensors. In this paper, we aim to interpret indoor scenes\nfrom one RGBD image. Our representation encodes the layout of orthogonal walls\nand the extent of objects, modeled with CAD-like 3D shapes. We parse both the\nvisible and occluded portions of the scene and all observable objects,\nproducing a complete 3D parse. Such a scene interpretation is useful for\nrobotics and visual reasoning, but difficult to produce due to the well-known\nchallenge of segmentation, the high degree of occlusion, and the diversity of\nobjects in indoor scenes. We take a data-driven approach, generating sets of\npotential object regions, matching to regions in training images, and\ntransferring and aligning associated 3D models while encouraging fit to\nobservations and spatial consistency. We use support inference to aid\ninterpretation and propose a retrieval scheme that uses convolutional neural\nnetworks (CNNs) to classify regions and retrieve objects with similar shapes.\nWe demonstrate the performance of our method on our newly annotated NYUd v2\ndataset with detailed 3D shapes.","url_abs":"http://arxiv.org/abs/1710.09490v2","url_pdf":"http://arxiv.org/pdf/1710.09490v2.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":"complete-3d-scene-parsing-from-an-rgbd-image","repo_url":"https://github.com/arron2003/rgbd2full3d","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"scene-parsing","task_name":"Scene Parsing"},{"task_slug":"visual-reasoning","task_name":"Visual Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}