Depth completion is the task of recovering dense depth maps from sparse ones, usually with the help of color images. Existing image-guided methods perform well on daytime depth perception self-driving benchmarks, but struggle in nighttime scenarios with poor visibility and complex illumination. To address these challenges, we propose a simple yet effective framework called LDCNet. Our key idea is to use Recurrent Inter-Convolution Differencing (RICD) and Illumination-Affinitive Intra-Convolution Differencing (IAICD) to enhance the nighttime color images and reduce the negative effects of the varying illumination, respectively. RICD explicitly estimates global illumination by differencing two convolutions with different kernels, treating the small-kernel-convolution feature as the center of the large-kernel-convolution feature in a new perspective. IAICD softly alleviates local relative light intensity by differencing a single convolution, where the center is dynamically aggregated based on neighboring pixels and the estimated illumination map in RICD. On both nighttime depth completion and depth estimation tasks, extensive experiments demonstrate the effectiveness of our LDCNet, reaching the state of the art.

翻译：深度补全是从稀疏深度图中恢复密集深度图的任务，通常借助彩色图像完成。现有的图像引导方法在白天深度感知自动驾驶基准测试中表现良好，但在能见度低且光照复杂的夜间场景中面临挑战。为解决这些问题，我们提出了一种简单而有效的框架LDCNet。其核心思想是分别采用循环跨卷积差分（RICD）和光照亲和性内卷积差分（IAICD），以增强夜间彩色图像并减少变化光照带来的负面影响。RICD通过差分具有不同核的两个卷积，显式估计全局光照，从新视角将小核卷积特征视为大核卷积特征的中心。IAICD通过差分单个卷积，柔和地缓解局部相对光强，其中中心根据相邻像素及RICD中估计的光照图动态聚合。在夜间深度补全和深度估计两项任务上的大量实验证明了LDCNet的有效性，达到了最先进水平。