{"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/roomnet-end-to-end-room-layout-estimation","title":"RoomNet: End-to-End Room Layout Estimation","arxiv_id":"1703.06241","date":"2017-03-18","proceeding":"ICCV 2017 10","authors":["Chen-Yu Lee","Vijay Badrinarayanan","Tomasz Malisiewicz","Andrew Rabinovich"],"abstract":"This paper focuses on the task of room layout estimation from a monocular RGB\nimage. Prior works break the problem into two sub-tasks: semantic segmentation\nof floor, walls, ceiling to produce layout hypotheses, followed by an iterative\noptimization step to rank these hypotheses. In contrast, we adopt a more direct\nformulation of this problem as one of estimating an ordered set of room layout\nkeypoints. The room layout and the corresponding segmentation is completely\nspecified given the locations of these ordered keypoints. We predict the\nlocations of the room layout keypoints using RoomNet, an end-to-end trainable\nencoder-decoder network. On the challenging benchmark datasets Hedau and LSUN,\nwe achieve state-of-the-art performance along with 200x to 600x speedup\ncompared to the most recent work. Additionally, we present optional extensions\nto the RoomNet architecture such as including recurrent computations and memory\nunits to refine the keypoint locations under the same parametric capacity.","url_abs":"http://arxiv.org/abs/1703.06241v2","url_pdf":"http://arxiv.org/pdf/1703.06241v2.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":"roomnet-end-to-end-room-layout-estimation","repo_url":"https://github.com/GitBoSun/roomnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"roomnet-end-to-end-room-layout-estimation","repo_url":"https://github.com/LucBourrat1/RoomNet-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"room-layout-estimation","task_name":"Room Layout Estimation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.06241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.06241"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/GitBoSun/roomnet","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/LucBourrat1/RoomNet-Pytorch","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"listed":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"0a2b6b4dcf009024","entry":"conv_bn_relu","repo":"LucBourrat1/RoomNet-Pytorch","repo_kind":"listed","path":"module/net.py","file_url":"https://github.com/LucBourrat1/RoomNet-Pytorch/blob/HEAD/module/net.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0a2b6b4dcf009024"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}