关键词: 单目深度估计, 低光照场景, 光照变换, 深度不变性约束

Abstract: A novel depth estimation algorithm based on illumination transformation and depth invariance constraint is proposed to solve the significantly degraded performance problem occurring in low-light scenarios, such as assisted driving at night. The key thought is to promote the depth estimation network’s low-light generalization ability through low-illumination diversity transformation and depth invariance constraint of the same scene, which can force the network to extract underlying light-independent depth features. Specifically, paired “RGB-Depth” dataset under well illumination is obtained with the SOTA depth estimation network at first. then, for each RGB image captured under good light, its illumination component is estimated and transformed referring to a good deal of low-light scene images to generate a series of low-light images with the same scene as it. Finally, the depth estimation network is fine-tuned using the depth invariance constraint between the generated low-light image and the original RGB image. The experimental results indicate that the proposed algorithm is superior to the original depth estimation algorithm（Lite-Mono）and the SOTA low-light depth estim-ation algorithms（STEPS and ADDS ） in all assessment criteria. In addition, the algorithm can be embedded into other classical depth estimation networks to improve the adaptive ability of the original algorithm conveniently.

Key words: monocular depth estimation, low-light scene, illumination transformation, depth invariance constraint