{"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/depthformer-multiscale-vision-transformer-for","title":"Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information Fusion","arxiv_id":"2207.04535","date":"2022-07-10","proceeding":null,"authors":["Ashutosh Agarwal","Chetan Arora"],"abstract":"Attention-based models such as transformers have shown outstanding performance on dense prediction tasks, such as semantic segmentation, owing to their capability of capturing long-range dependency in an image. However, the benefit of transformers for monocular depth prediction has seldom been explored so far. This paper benchmarks various transformer-based models for the depth estimation task on an indoor NYUV2 dataset and an outdoor KITTI dataset. We propose a novel attention-based architecture, Depthformer for monocular depth estimation that uses multi-head self-attention to produce the multiscale feature maps, which are effectively combined by our proposed decoder network. We also propose a Transbins module that divides the depth range into bins whose center value is estimated adaptively per image. The final depth estimated is a linear combination of bin centers for each pixel. Transbins module takes advantage of the global receptive field using the transformer module in the encoding stage. Experimental results on NYUV2 and KITTI depth estimation benchmark demonstrate that our proposed method improves the state-of-the-art by 3.3%, and 3.3% respectively in terms of Root Mean Squared Error (RMSE). Code is available at https://github.com/ashutosh1807/Depthformer.git.","url_abs":"https://arxiv.org/abs/2207.04535v2","url_pdf":"https://arxiv.org/pdf/2207.04535v2.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":"depthformer-multiscale-vision-transformer-for","repo_url":"https://github.com/ashutosh1807/depthformer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"depth-prediction","task_name":"Depth Prediction"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/monocular-depth-estimation-on-kitti-eigen","task":"Monocular Depth Estimation","dataset":"KITTI Eigen split","model":"Depthformer","rank_in_archive_order":34,"of":79,"metrics":{"Delta < 1.25":"0.967","Delta < 1.25^2":"0.996","Delta < 1.25^3":"0.999","RMSE":"2.285","RMSE log":" 0.087","Sq Rel":"0.187","absolute relative error":"0.058"},"uses_additional_data":true},{"leaderboard":"/sota/monocular-depth-estimation-on-nyu-depth-v2","task":"Monocular Depth Estimation","dataset":"NYU-Depth V2","model":"Depthformer","rank_in_archive_order":49,"of":85,"metrics":{"Delta < 1.25":"0.913","Delta < 1.25^2":"0.988","Delta < 1.25^3":"0.997","RMSE":"0.345","absolute relative error":"0.100","log 10":"0.042"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2207.04535","atlas_url":"https://app.syntology.ai/?focus=2207.04535","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.04535"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. 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