{"id":118495,"date":"2021-10-31T23:25:00","date_gmt":"2021-10-31T14:25:00","guid":{"rendered":"http:\/\/175.125.95.178\/ai-in-signal\/18495\/"},"modified":"2026-04-09T17:51:49","modified_gmt":"2026-04-09T08:51:49","slug":"18495","status":"publish","type":"ai-in-signal","link":"http:\/\/ee.presscat.kr\/en\/ai-in-signal\/18495\/","title":{"rendered":"Non-Local Spatial Propagation Network for Depth Completion (Prof. In-So Kweon)"},"content":{"rendered":"<p style=\"text-align:justify;margin-bottom:11px\"><span style=\"font-size:10pt\"><span style=\"line-height:107%\"><span>Conference\/Journal, Year: ECCV, 2020<\/span><\/span><\/span><\/p>\n<p align=\"left\" style=\"text-align:left\"><span style=\"font-size:10pt\"><span style=\"line-height:normal\"><span><span><span>In this paper, we propose a robust and efficient end-to-end non-local spatial propagation network for depth completion. The proposed network takes RGB and sparse depth images as inputs and estimates non-local neighbors and their affinities of each pixel, as well as an initial depth map with pixel-wise confidences. The initial depth prediction is then iteratively refined by its confidence and non-local spatial propagation procedure based on the predicted non-local neighbors and corresponding affinities. Unlike previous algorithms that utilize fixedlocal neighbors, the proposed algorithm effectively avoids irrelevant local neighbors and concentrates on relevant non-local neighbors during propagation. In addition, we introduce a learnable affinity normalization to better learn the affinity combinations compared to conventional methods. The proposed algorithm is inherently robust to the mixed-depth problem on depth boundaries, which is one of the major issues for existing depth estimation\/completion algorithms. Experimental results on indoor and outdoor datasets demonstrate that the proposed algorithm is superior to conventional algorithms in terms of depth completion accuracy and robustness to the mixed-depth problem. Our implementation is publicly available on the project page.<\/span><\/span><\/span><\/span><\/span><\/p>\n<p align=\"left\" style=\"text-align:left\"><span style=\"font-size:10pt\"><span style=\"line-height:normal\"><span><span><span><\/p>\n<div class=\"\"><img decoding=\"async\" class=\"\" src=\"\/wp-content\/uploads\/drupal\/\uad8c\uc778\uc18c\uad50\uc218\ub2d814.png\" alt=\"\" title=\"\"><\/div>\n<p><\/span><\/span><\/span><\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>675<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-118495","ai-in-signal","type-ai-in-signal","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-signal\/118495","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-signal"}],"about":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/types\/ai-in-signal"}],"wp:attachment":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/media?parent=118495"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}