Structure Selective Depth Superresolution for RGB-D Cameras

Self-supervised depth super-resolution with contrastive multiview pre-training

Many low-level vision tasks, including guided depth super-resolution (GDSR), struggle with the issue of insufficient paired training data. Self-supervised learning is a promising solution, but it remains challenging to upsample depth maps without the explicit supervision of high-resolution target images. To alleviate this problem, we propose a self-supervised depth super-resolution method with contrastive multiview pre-training. Unlike existing contrastive learning methods for classification or segmentation tasks, our strategy can be applied to regression tasks even when trained on a small-scale dataset and can reduce information redundancy by extracting unique features from the guide. Furthermore, we propose a novel mutual modulation scheme that can effectively compute the local spatial correlation between cross-modal features. Exhaustive experiments demonstrate that our method attains superior performance with respect to state-of-the-art GDSR methods and exhibits good generalization to other modalities.

Computational Large Field-of-View RGB-D Integral Imaging System

The integral imaging system has received considerable research attention because it can be applied to real-time three-dimensional image displays with a continuous view angle without supplementary devices. Most previous approaches place a physical micro-lens array in front of the image, where each lens looks different depending on the viewing angle. A computational integral imaging system with a virtual micro-lens arrays has been proposed in order to provide flexibility for users to change micro-lens arrays and focal length while reducing distortions due to physical mismatches with the lens arrays. However, computational integral imaging methods only represent part of the whole image because the size of virtual lens arrays is much smaller than the given large-scale images when dealing with large-scale images. As a result, the previous approaches produce sub-aperture images with a small field of view and need additional devices for depth information to apply to integral imaging pickup systems. In this paper, we present a single image-based computational RGB-D integral imaging pickup system for a large field of view in real time. The proposed system comprises three steps: deep learning-based automatic depth map estimation from an RGB input image without the help of an additional device, a hierarchical integral imaging system for a large field of view in real time, and post-processing for optimized visualization of the failed pickup area using an inpainting method. Quantitative and qualitative experimental results verify the proposed approach’s robustness.