Non-Learning Stereo-Aided Depth Completion Under Mis-Projection via Selective Stereo Matching

Non-Learning Stereo-Aided Depth Completion Under Mis-Projection via Selective Stereo Matching

We propose a non-learning depth completion method for a sparse depth map captured using a light detection and ranging (LiDAR) sensor guided by a pair of stereo images. Generally, conventional stereo-aided depth completion methods have two limiations. (i) they assume the given sparse depth map is acc...

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Bibliographic Details
Main Authors: Yasuhiro Yao, Ryoichi Ishikawa, Shingo Ando, Kana Kurata, Naoki Ito, Jun Shimamura, Takeshi Oishi
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Computer vision
depth completion
LiDAR
sensor fusion
stereo matching
Online Access:https://ieeexplore.ieee.org/document/9558759/

Internet

https://ieeexplore.ieee.org/document/9558759/

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