{"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/sparse-and-noisy-lidar-completion-with-rgb-1","title":"Sparse and noisy LiDAR completion with RGB guidance anduncertainty","arxiv_id":null,"date":"2019-02-14","proceeding":"arXiv 2019 2","authors":["Wouter Van Gansbeke","Davy Neven","Bert de Brabandere","Luc van Gool"],"abstract":"his work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images.  For autonomous  vehicles  and  robotics  the  use  of  LiDAR is  indispensable  in  order  to  achieve  precise  depth  predictions.   A  multitude  of  applications  depend  on  the awareness  of  their  surroundings,  and  use  depth  cues to  reason  and  react  accordingly.    On  the  one  hand, monocular depth prediction methods fail to generate absolute  and  precise  depth  maps.    On  the  other  hand, stereoscopic  approaches  are  still  significantly  outperformed  by  LiDAR  based  approaches.   The  goal  of  the depth completion task is to generate dense depth predictions from sparse and irregular point clouds which are mapped  to  a  2D  plane.  We  propose  a  new  framework which extracts both global and local information in order to produce proper depth maps.  We argue that simple  depth  completion  does  not  require  a  deep  network. However, we additionally propose a fusion method with RGB  guidance  from  a  monocular  camera  in  order  to leverage  object  information  and  to  correct  mistakes  in the  sparse  input.   This  improves  the  accuracy  significantly.   Moreover,  confidence  masks  are  exploited  in order to take into account the uncertainty in the depth predictions  from  each  modality.    This  fusion  method outperforms  the  state-of-the-art  and  ranks  first  on  the KITTI  depth  completion  benchmark.","url_abs":"https://arxiv.org/abs/1902.05356","url_pdf":"https://arxiv.org/pdf/1902.05356.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":"sparse-and-noisy-lidar-completion-with-rgb-1","repo_url":"https://github.com/wvangansbeke/Sparse-Depth-Completion","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"depth-completion","task_name":"Depth Completion"},{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"depth-prediction","task_name":"Depth Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}