Depth Superresolution using Motion Adaptive Regularization

TL;DR

This paper introduces a novel method for depth superresolution in videos by leveraging motion-adaptive regularization to utilize space-time redundancy, significantly enhancing depth quality.

Contribution

It proposes a new approach that incorporates temporal information and motion-adaptive low-rank regularization for improved depth resolution from videos.

Findings

Substantially improves high-resolution depth estimation quality.

Utilizes space-time redundancy effectively.

Can serve as a foundational component in vision systems requiring high-res depth.

Abstract

Spatial resolution of depth sensors is often significantly lower compared to that of conventional optical cameras. Recent work has explored the idea of improving the resolution of depth using higher resolution intensity as a side information. In this paper, we demonstrate that further incorporating temporal information in videos can significantly improve the results. In particular, we propose a novel approach that improves depth resolution, exploiting the space-time redundancy in the depth and intensity using motion-adaptive low-rank regularization. Experiments confirm that the proposed approach substantially improves the quality of the estimated high-resolution depth. Our approach can be a first component in systems using vision techniques that rely on high resolution depth information.