{"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/fastdepth-fast-monocular-depth-estimation-on","title":"FastDepth: Fast Monocular Depth Estimation on Embedded Systems","arxiv_id":"1903.03273","date":"2019-03-08","proceeding":null,"authors":["Diana Wofk","Fangchang Ma","Tien-Ju Yang","Sertac Karaman","Vivienne Sze"],"abstract":"Depth sensing is a critical function for robotic tasks such as localization,\nmapping and obstacle detection. There has been a significant and growing\ninterest in depth estimation from a single RGB image, due to the relatively low\ncost and size of monocular cameras. However, state-of-the-art single-view depth\nestimation algorithms are based on fairly complex deep neural networks that are\ntoo slow for real-time inference on an embedded platform, for instance, mounted\non a micro aerial vehicle. In this paper, we address the problem of fast depth\nestimation on embedded systems. We propose an efficient and lightweight\nencoder-decoder network architecture and apply network pruning to further\nreduce computational complexity and latency. In particular, we focus on the\ndesign of a low-latency decoder. Our methodology demonstrates that it is\npossible to achieve similar accuracy as prior work on depth estimation, but at\ninference speeds that are an order of magnitude faster. Our proposed network,\nFastDepth, runs at 178 fps on an NVIDIA Jetson TX2 GPU and at 27 fps when using\nonly the TX2 CPU, with active power consumption under 10 W. FastDepth achieves\nclose to state-of-the-art accuracy on the NYU Depth v2 dataset. To the best of\nthe authors' knowledge, this paper demonstrates real-time monocular depth\nestimation using a deep neural network with the lowest latency and highest\nthroughput on an embedded platform that can be carried by a micro aerial\nvehicle.","url_abs":"http://arxiv.org/abs/1903.03273v1","url_pdf":"http://arxiv.org/pdf/1903.03273v1.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":"fastdepth-fast-monocular-depth-estimation-on","repo_url":"https://github.com/dwofk/fast-depth","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"},{"task_slug":"network-pruning","task_name":"Network Pruning"}],"methods":[{"method_slug":"pruning","method_name":"Pruning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1903.03273","atlas_url":"https://app.syntology.ai/?focus=1903.03273","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.03273"}},"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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