Depth Map Super-Resolution via Deep Cross-modality and Cross-scale Guidance

Guided depth super-resolution is essential in many applications, which enhances low-resolution (LR) depth maps using high-resolution (HR) RGB images from the same scene. However, the challenge lies in avoiding the texture-copy artifacts issue caused by structural inconsistencies between two modalities. To mitigate, we propose a cross-modality and cross-scale guided depth super-resolution network (D2CNet). We first design a novel two-stage feature integration module to effectively fuse multi-modal RGB and depth while minimizing texture-copy artifacts. That is, a cross-modality fusion stage transfers consistent structures from RGB to depth in a multi-scale manner, and a cross-scale refinement stage mitigates inconsistent structures across modalities. In addition, we design a convolution group as the basic module to well extract high-frequency features and an LR and HR domain projection strategy to enrich features between the fusion and refinement stages. We then develop a new network architecture by progressively repeating the feature integration module and the convolution group, which is flexibly controllable to strike a balance between accuracy and cost for easy implementation in real world. Extensive experiments on multiple benchmarks demonstrate that our D2CNet consistently achieves superior accuracy and generalization ability across sampling scales in both qualitative and quantitative evaluations, when compared to state-of-the-art baselines. The code is at https://github.com/suzdl/d2cnet