Depth Superresolution using Motion Adaptive Regularization
This work addresses the need for high-resolution depth information in vision systems, though it is incremental as it builds on existing methods that use intensity data.
The paper tackles the problem of low spatial resolution in depth sensors by incorporating temporal information from videos to improve depth superresolution, resulting in a substantial improvement in the quality of estimated high-resolution depth.
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.