{"url":"/sota/depth-completion-on-void","task":{"name":"Depth Completion","url":"/task/depth-completion","note":null},"dataset":{"name":"VOID","url":"/dataset/void"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"The **Depth Completion** task is a sub-problem of depth estimation. In the sparse-to-dense depth completion problem, one wants to infer the dense depth map of a 3-D scene given an RGB image and its corresponding sparse reconstruction in the form of a sparse depth map obtained either from computational methods such as SfM (Strcuture-from-Motion) or active sensors such as lidar or structured light sensors. \r\n\r\n<span class=\"description-source\">Source: [LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery ](https://arxiv.org/abs/1905.02744)</span>,\r\n<span class=\"description-source\">[Unsupervised Depth Completion from Visual Inertial Odometry](https://arxiv.org/abs/1905.08616)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["MAE","RMSE","iMAE","iRMSE"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"MAE":"lower","RMSE":"lower","iMAE":null,"iRMSE":null}},"counts":{"rows":6,"rows_with_code":5,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"NLSPN","metrics":{"MAE":"26.736","RMSE":"79.121","iMAE":"12.703","iRMSE":"33.876"},"uses_additional_data":false,"paper_date":"2020-07-20","paper":"/paper/non-local-spatial-propagation-network-for","paper_url":"https://arxiv.org/abs/2007.10042v1","paper_title":"Non-Local Spatial Propagation Network for Depth Completion","code":"https://github.com/zzangjinsun/NLSPN_ECCV20","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":4,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"KBNet","metrics":{"MAE":"39.80","RMSE":"95.86","iMAE":"21.16","iRMSE":"49.72"},"uses_additional_data":false,"paper_date":"2021-08-24","paper":"/paper/unsupervised-depth-completion-with-calibrated","paper_url":"https://arxiv.org/abs/2108.10531v2","paper_title":"Unsupervised Depth Completion with Calibrated Backprojection Layers","code":"https://github.com/alexklwong/calibrated-backprojection-network","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"ScaffNet-FusionNet","metrics":{"MAE":"59.53","RMSE":"119.14","iMAE":"35.72","iRMSE":"68.36"},"uses_additional_data":false,"paper_date":"2021-06-06","paper":"/paper/learning-topology-from-synthetic-data-for","paper_url":"https://arxiv.org/abs/2106.02994v3","paper_title":"Learning Topology from Synthetic Data for Unsupervised Depth Completion","code":"https://github.com/alexklwong/learning-topology-synthetic-data","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"VOICED","metrics":{"MAE":"85.05","RMSE":"169.79","iMAE":"48.92","iRMSE":"104.02"},"uses_additional_data":false,"paper_date":"2019-05-15","paper":"/paper/190508616","paper_url":"https://arxiv.org/abs/1905.08616v4","paper_title":"Unsupervised Depth Completion from Visual Inertial Odometry","code":"https://github.com/alexklwong/unsupervised-depth-completion-visual-inertial-odometry","n_code_links":2,"syntology":null},{"rank_in_archive_order":5,"model":"DDP","metrics":{"MAE":"151.86","RMSE":"222.36","iMAE":"74.59","iRMSE":"112.36"},"uses_additional_data":false,"paper_date":"2019-01-28","paper":"/paper/dense-depth-posterior-ddp-from-single-image","paper_url":"http://arxiv.org/abs/1901.10034v2","paper_title":"Dense Depth Posterior (DDP) from Single Image and Sparse Range","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":6,"model":"SS-S2D","metrics":{"MAE":"178.85","RMSE":"243.84","iMAE":"80.12","iRMSE":"107.69"},"uses_additional_data":false,"paper_date":"2018-07-01","paper":"/paper/self-supervised-sparse-to-dense-self","paper_url":"http://arxiv.org/abs/1807.00275v2","paper_title":"Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera","code":"https://github.com/fangchangma/self-supervised-depth-completion","n_code_links":2,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":0}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":2,"rows_with_any_sample_ran":2,"distinct_papers_with_graph_line":2,"distinct_papers_with_any_sample_ran":2,"samples_over_distinct_papers":{"n_ran":3,"n_unverified":4,"n_samples":7,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":3,"n_unverified":4,"n_samples":7,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}