{"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/tackling-3d-tof-artifacts-through-learning","title":"Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset","arxiv_id":"1807.10376","date":"2018-07-26","proceeding":"ECCV 2018 9","authors":["Qi Guo","Iuri Frosio","Orazio Gallo","Todd Zickler","Jan Kautz"],"abstract":"Scene motion, multiple reflections, and sensor noise introduce artifacts in\nthe depth reconstruction performed by time-of-flight cameras. We propose a\ntwo-stage, deep-learning approach to address all of these sources of artifacts\nsimultaneously. We also introduce FLAT, a synthetic dataset of 2000 ToF\nmeasurements that capture all of these nonidealities, and allows to simulate\ndifferent camera hardware. Using the Kinect 2 camera as a baseline, we show\nimproved reconstruction errors over state-of-the-art methods, on both simulated\nand real data.","url_abs":"http://arxiv.org/abs/1807.10376v1","url_pdf":"http://arxiv.org/pdf/1807.10376v1.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":[],"methods":[],"datasets_introduced":[{"slug":"flat","name":"FLAT","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.10376","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}