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of 449
pro vyhledávání: '"Depth completion"'
Autor:
Zhi Liu, Cheolkon Jung
Publikováno v:
IEEE Access, Vol 12, Pp 18189-18197 (2024)
Depth completion aims to recover dense depth maps from sparse depth maps. Recent approaches have used additional modalities as guidance to improve depth completion performance. Image-guided depth completion uses scene information from color images, b
Externí odkaz:
https://doaj.org/article/52018cd21f7446f99f541764751e912f
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 5, Pp 204-212 (2024)
Many image-based rendering (IBR) methods rely on depth estimates obtained from structured light or time-of-flight depth sensors to synthesize novel views from sparse camera networks. However, these estimates often contain missing or noisy regions, re
Externí odkaz:
https://doaj.org/article/2e1aa1810db0457a829ee68aeb45e168
Publikováno v:
Sensors, Vol 24, Iss 19, p 6270 (2024)
Depth information is crucial for perceiving three-dimensional scenes. However, depth maps captured directly by depth sensors are often incomplete and noisy, our objective in the depth-completion task is to generate dense and accurate depth maps from
Externí odkaz:
https://doaj.org/article/097d03781f5d43039cfd1925daede9e6
Publikováno v:
Algorithms, Vol 17, Iss 7, p 298 (2024)
The development of a sparse-invariant depth completion model capable of handling varying levels of input depth sparsity is highly desirable in real-world applications. However, existing sparse-invariant models tend to degrade when the input depth poi
Externí odkaz:
https://doaj.org/article/7cc54f0613514b7c908fd4d6d4e20cde
Autor:
Vanel Lazcano, Felipe Calderero
Publikováno v:
Applied Sciences, Vol 14, Iss 11, p 4514 (2024)
Depth map estimation is crucial for a wide range of applications. Unfortunately, it often presents missing or unreliable data. The objective of depth completion is to fill in the “holes” in a depth map by propagating the depth information using g
Externí odkaz:
https://doaj.org/article/1de46a18df2b41b68601646ba8f1561a
Publikováno v:
Sensors, Vol 24, Iss 7, p 2374 (2024)
Fusing multiple sensor perceptions, specifically LiDAR and camera, is a prevalent method for target recognition in autonomous driving systems. Traditional object detection algorithms are limited by the sparse nature of LiDAR point clouds, resulting i
Externí odkaz:
https://doaj.org/article/2395ebe1a9b3469f8203b3e116ce28df
Akademický článek
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Publikováno v:
IEEE Access, Vol 11, Pp 111347-111357 (2023)
In order to acquire precise depth maps, depth completion is a fundamental method for autonomous vehicles and robotics. Recent methods mainly focus on fusing multi-model information from sparse depth maps and color images to recover dense depth maps.
Externí odkaz:
https://doaj.org/article/f62e1049069d454db4113a7adada4bbe
Autor:
Adriano Cardace, Andrea Conti, Pierluigi Zama Ramirez, Riccardo Spezialetti, Samuele Salti, Luigi Di Stefano
Publikováno v:
IEEE Access, Vol 11, Pp 85155-85164 (2023)
LiDAR semantic segmentation is receiving increased attention due to its deployment in autonomous driving applications. As LiDARs come often with other sensors such as RGB cameras, multi-modal approaches for this task have been developed, which howeve
Externí odkaz:
https://doaj.org/article/df809a58092547a1b3a3b365302822a1
Publikováno v:
IEEE Access, Vol 11, Pp 78251-78261 (2023)
Image-guided depth completion aims to generate dense depth maps from sparse depth maps guided by their corresponding color (RGB) images. In this paper, we propose deep sparse depth completion using multi-affinity matrix. Recently, spatial propagation
Externí odkaz:
https://doaj.org/article/c29b75178e724394afb21f3ffd22e3c2