{"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/glpanodepth-global-to-local-panoramic-depth","title":"GLPanoDepth: Global-to-Local Panoramic Depth Estimation","arxiv_id":"2202.02796","date":"2022-02-06","proceeding":null,"authors":["Jiayang Bai","Shuichang Lai","Haoyu Qin","Jie Guo","Yanwen Guo"],"abstract":"In this paper, we propose a learning-based method for predicting dense depth values of a scene from a monocular omnidirectional image. An omnidirectional image has a full field-of-view, providing much more complete descriptions of the scene than perspective images. However, fully-convolutional networks that most current solutions rely on fail to capture rich global contexts from the panorama. To address this issue and also the distortion of equirectangular projection in the panorama, we propose Cubemap Vision Transformers (CViT), a new transformer-based architecture that can model long-range dependencies and extract distortion-free global features from the panorama. We show that cubemap vision transformers have a global receptive field at every stage and can provide globally coherent predictions for spherical signals. To preserve important local features, we further design a convolution-based branch in our pipeline (dubbed GLPanoDepth) and fuse global features from cubemap vision transformers at multiple scales. This global-to-local strategy allows us to fully exploit useful global and local features in the panorama, achieving state-of-the-art performance in panoramic depth estimation.","url_abs":"https://arxiv.org/abs/2202.02796v2","url_pdf":"https://arxiv.org/pdf/2202.02796v2.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":"glpanodepth-global-to-local-panoramic-depth","repo_url":"https://github.com/LeoDarcy/GLPanoDepth","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/depth-estimation-on-stanford2d3d-panoramic","task":"Depth Estimation","dataset":"Stanford2D3D Panoramic","model":"GLPanoDepth","rank_in_archive_order":7,"of":18,"metrics":{"RMSE":"0.3493"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2202.02796","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.02796"}},"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/LeoDarcy/GLPanoDepth","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_draft_wrong":1,"ran_violates":1,"unverified":2},"by_repo_kind":{"official":{"samples":4,"ran":2,"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":0,"samples":[{"code_sha256_prefix":"158bf4c3a5f11f04","entry":"conv1x1","repo":"LeoDarcy/GLPanoDepth","repo_kind":"official","path":"models/TwoBranch.py","file_url":"https://github.com/LeoDarcy/GLPanoDepth/blob/HEAD/models/TwoBranch.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"158bf4c3a5f11f04"}},{"code_sha256_prefix":"6ba8cee9f5daea41","entry":"pair","repo":"LeoDarcy/GLPanoDepth","repo_kind":"official","path":"models/selftransnet/model_utils.py","file_url":"https://github.com/LeoDarcy/GLPanoDepth/blob/HEAD/models/selftransnet/model_utils.py","link_basis":"harvester_set","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6ba8cee9f5daea41"}},{"code_sha256_prefix":"2647adec2b8e3196","entry":"conv3x3","repo":"LeoDarcy/GLPanoDepth","repo_kind":"official","path":"models/TwoBranch.py","file_url":"https://github.com/LeoDarcy/GLPanoDepth/blob/HEAD/models/TwoBranch.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2647adec2b8e3196"}},{"code_sha256_prefix":"e94322673236e329","entry":"read_list","repo":"LeoDarcy/GLPanoDepth","repo_kind":"official","path":"datasets/matterport3d.py","file_url":"https://github.com/LeoDarcy/GLPanoDepth/blob/HEAD/datasets/matterport3d.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e94322673236e329"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}