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arXiv · 2202.12108

Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion

Hyeonsoo Jang·Yeongmin Ko·Younkwan Lee·Moongu Jeon
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Abstract

Depth estimation plays a important role in SLAM, odometry, and autonomous driving. Especially, monocular depth estimation is profitable technology because of its low cost, memory, and computation. However, it is not a sufficiently predicting depth map due to a camera often failing to get a clean image because of light conditions. To solve this problem, various sensor fusion method has been proposed. Even though it is a powerful method, sensor fusion requires expensive sensors, additional memory, and high computational performance. In this paper, we present color image and monochrome image pixel-level fusion and stereo matching with partially enhanced correlation coefficient maximization. Our methods not only outperform the state-of-the-art works across all metrics but also efficient in terms of cost, memory, and computation. We also validate the effectiveness of our design with an ablation study.

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Hyeonsoo Jang, Yeongmin Ko, Younkwan Lee, Moongu Jeon. 2022-02-24. Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion. https://arxiv.org/abs/2202.12108

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