{"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/rgb-d-local-implicit-function-for-depth","title":"RGB-D Local Implicit Function for Depth Completion of Transparent Objects","arxiv_id":"2104.00622","date":"2021-04-01","proceeding":"CVPR 2021 1","authors":["Luyang Zhu","Arsalan Mousavian","Yu Xiang","Hammad Mazhar","Jozef van Eenbergen","Shoubhik Debnath","Dieter Fox"],"abstract":"Majority of the perception methods in robotics require depth information provided by RGB-D cameras. However, standard 3D sensors fail to capture depth of transparent objects due to refraction and absorption of light. In this paper, we introduce a new approach for depth completion of transparent objects from a single RGB-D image. Key to our approach is a local implicit neural representation built on ray-voxel pairs that allows our method to generalize to unseen objects and achieve fast inference speed. Based on this representation, we present a novel framework that can complete missing depth given noisy RGB-D input. We further improve the depth estimation iteratively using a self-correcting refinement model. To train the whole pipeline, we build a large scale synthetic dataset with transparent objects. Experiments demonstrate that our method performs significantly better than the current state-of-the-art methods on both synthetic and real world data. In addition, our approach improves the inference speed by a factor of 20 compared to the previous best method, ClearGrasp. Code and dataset will be released at https://research.nvidia.com/publication/2021-03_RGB-D-Local-Implicit.","url_abs":"https://arxiv.org/abs/2104.00622v1","url_pdf":"https://arxiv.org/pdf/2104.00622v1.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":"rgb-d-local-implicit-function-for-depth","repo_url":"https://github.com/NVlabs/implicit_depth","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-completion","task_name":"Depth Completion"},{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"transparent-objects","task_name":"Transparent objects"}],"methods":[],"datasets_introduced":[{"slug":"omiverse-object-dataset","name":"Omiverse Object dataset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2104.00622","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}