Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions

• URL: http://arxiv.org/abs/2411.03638v1
• Date: Wed, 06 Nov 2024 03:30:46 GMT
• Title: Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions
• Authors: Zihan Qin, Jialei Xu, Wenbo Zhao, Junjun Jiang, Xianming Liu,
• Abstract summary: We propose a novel framework incorporating stereo depth estimation to enforce accurate geometric constraints. To mitigate the effects of poor lighting on stereo matching, we introduce Degradation Masking. Our method achieves state-of-the-art (SOTA) performance on the Multi-Spectral Stereo (MS2) dataset.
• Score: 58.88917836512819
• License:
• Abstract: Depth estimation under adverse conditions remains a significant challenge. Recently, multi-spectral depth estimation, which integrates both visible light and thermal images, has shown promise in addressing this issue. However, existing algorithms struggle with precise pixel-level feature matching, limiting their ability to fully exploit geometric constraints across different spectra. To address this, we propose a novel framework incorporating stereo depth estimation to enforce accurate geometric constraints. In particular, we treat the visible light and thermal images as a stereo pair and utilize a Cross-modal Feature Matching (CFM) Module to construct a cost volume for pixel-level matching. To mitigate the effects of poor lighting on stereo matching, we introduce Degradation Masking, which leverages robust monocular thermal depth estimation in degraded regions. Our method achieves state-of-the-art (SOTA) performance on the Multi-Spectral Stereo (MS2) dataset, with qualitative evaluations demonstrating high-quality depth maps under varying lighting conditions.

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