{"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/detaching-and-boosting-dual-engine-for-scale","title":"Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth Estimation","arxiv_id":"2210.03952","date":"2022-10-08","proceeding":null,"authors":["Peizhe Jiang","Wei Yang","Xiaoqing Ye","Xiao Tan","Meng Wu"],"abstract":"Monocular depth estimation (MDE) in the self-supervised scenario has emerged as a promising method as it refrains from the requirement of ground truth depth. Despite continuous efforts, MDE is still sensitive to scale changes especially when all the training samples are from one single camera. Meanwhile, it deteriorates further since camera movement results in heavy coupling between the predicted depth and the scale change. In this paper, we present a scale-invariant approach for self-supervised MDE, in which scale-sensitive features (SSFs) are detached away while scale-invariant features (SIFs) are boosted further. To be specific, a simple but effective data augmentation by imitating the camera zooming process is proposed to detach SSFs, making the model robust to scale changes. Besides, a dynamic cross-attention module is designed to boost SIFs by fusing multi-scale cross-attention features adaptively. Extensive experiments on the KITTI dataset demonstrate that the detaching and boosting strategies are mutually complementary in MDE and our approach achieves new State-of-The-Art performance against existing works from 0.097 to 0.090 w.r.t absolute relative error. The code will be made public soon.","url_abs":"https://arxiv.org/abs/2210.03952v2","url_pdf":"https://arxiv.org/pdf/2210.03952v2.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":"detaching-and-boosting-dual-engine-for-scale","repo_url":"https://github.com/attackonmuggle/dab_net0","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2210.03952","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.03952"}},"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":"deterministic:regex_extraction","url":"https://github.com/AttackonMuggle/DaB","reach":{"status":"gone","observed_at":"2026-09-17","how":"tree_404+repo_404"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/attackonmuggle/dab_net0","reach":null}],"summary":{"ran":5,"unverified":1},"by_repo_kind":{"official":{"samples":6,"ran":5,"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":6,"samples":[{"code_sha256_prefix":"6e605fe157361125","entry":"Attention","repo":"attackonmuggle/dab_net0","repo_kind":"official","path":"Pytorch/mono/model/mono_hrnet/depth_decoder.py","file_url":"https://github.com/attackonmuggle/dab_net0/blob/HEAD/Pytorch/mono/model/mono_hrnet/depth_decoder.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6e605fe157361125"}},{"code_sha256_prefix":"122d3995efe2fb09","entry":"ChannelWiseAttention","repo":"attackonmuggle/dab_net0","repo_kind":"official","path":"Pytorch/mono/model/mono_hrnet/depth_decoder.py","file_url":"https://github.com/attackonmuggle/dab_net0/blob/HEAD/Pytorch/mono/model/mono_hrnet/depth_decoder.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"122d3995efe2fb09"}},{"code_sha256_prefix":"d7d5772ff6184744","entry":"ConvELU","repo":"attackonmuggle/dab_net0","repo_kind":"official","path":"Pytorch/mono/model/mono_hrnet/depth_decoder.py","file_url":"https://github.com/attackonmuggle/dab_net0/blob/HEAD/Pytorch/mono/model/mono_hrnet/depth_decoder.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"d7d5772ff6184744"}},{"code_sha256_prefix":"0da7ef4fe6ca7881","entry":"DCAM","repo":"attackonmuggle/dab_net0","repo_kind":"official","path":"Pytorch/mono/model/mono_hrnet/depth_decoder.py","file_url":"https://github.com/attackonmuggle/dab_net0/blob/HEAD/Pytorch/mono/model/mono_hrnet/depth_decoder.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0da7ef4fe6ca7881"}},{"code_sha256_prefix":"955507cfff05bbf8","entry":"softmax_Attention","repo":"attackonmuggle/dab_net0","repo_kind":"official","path":"Pytorch/mono/model/mono_hrnet/depth_decoder.py","file_url":"https://github.com/attackonmuggle/dab_net0/blob/HEAD/Pytorch/mono/model/mono_hrnet/depth_decoder.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"955507cfff05bbf8"}},{"code_sha256_prefix":"0c455bd18d9aeeeb","entry":"DepthDecoder","repo":"attackonmuggle/dab_net0","repo_kind":"official","path":"Pytorch/mono/model/mono_hrnet/depth_decoder.py","file_url":"https://github.com/attackonmuggle/dab_net0/blob/HEAD/Pytorch/mono/model/mono_hrnet/depth_decoder.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0c455bd18d9aeeeb"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}