CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution

Aditya Kasliwal, Ishaan Gakhar, Aryan Bhavin Kamani

Abstract: Consumer-grade depth sensors provide low-resolution depth maps; however, a high-resolution RGB camera is usually mounted on the same device and acquires a high-resolution image of the same scene. While deep learning and guided filtering methods gave decent results, recent works have highlighted the superiority of using RGB images for Depth Super Resolution. This paper proposes CoReGAN, a generative data fusion model that employs contrastive learning to regularize the extracted features of 2 independent encoders and 1 decoder for Guided Depth Super Resolution, demonstrating state-of-the-art results.