{"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/superdepth-self-supervised-super-resolved","title":"SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation","arxiv_id":"1810.01849","date":"2018-10-03","proceeding":null,"authors":["Sudeep Pillai","Rares Ambrus","Adrien Gaidon"],"abstract":"Recent techniques in self-supervised monocular depth estimation are\napproaching the performance of supervised methods, but operate in low\nresolution only. We show that high resolution is key towards high-fidelity\nself-supervised monocular depth prediction. Inspired by recent deep learning\nmethods for Single-Image Super-Resolution, we propose a sub-pixel convolutional\nlayer extension for depth super-resolution that accurately synthesizes\nhigh-resolution disparities from their corresponding low-resolution\nconvolutional features. In addition, we introduce a differentiable\nflip-augmentation layer that accurately fuses predictions from the image and\nits horizontally flipped version, reducing the effect of left and right shadow\nregions generated in the disparity map due to occlusions. Both contributions\nprovide significant performance gains over the state-of-the-art in\nself-supervised depth and pose estimation on the public KITTI benchmark. A\nvideo of our approach can be found at https://youtu.be/jKNgBeBMx0I.","url_abs":"http://arxiv.org/abs/1810.01849v1","url_pdf":"http://arxiv.org/pdf/1810.01849v1.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":[],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"depth-prediction","task_name":"Depth Prediction"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/monocular-depth-estimation-on-kitti-eigen-1","task":"Monocular Depth Estimation","dataset":"KITTI Eigen split unsupervised","model":"SuperDepth S","rank_in_archive_order":49,"of":55,"metrics":{"absolute relative error":"0.112"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.01849","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}