Search

Guided depth super-resolution aims at using a low-resolution depth map and an associated high-resolution RGB image to recover a high-resolution depth map. However, restoring precise and sharp edges near depth discontinuities and fine structures is still challenging for state-of-the-art methods. To alleviate this issue, we propose a novel multi-stage depth super-resolution network, which progressively reconstructs HR depth maps from explicit and implicit high-frequency information. We introduce an efficient transformer to obtain explicit high-frequency information. The shape bias and global context of the transformer allow our model to focus on high-frequency details between objects, i.e., depth discontinuities, rather than texture within objects. Furthermore, we project the input color images into the frequency domain for additional implicit high-frequency cues extraction. Finally, to incorporate the structural details, we develop a fusion strategy that combines depth features and high-frequency information in the multi-stage-scale framework. Exhaustive experiments on the main benchmarks show that our approach establishes a new state-of-the-art. Code will be publicly available at https://github.com/wudiqx106/DSR-EI. • DSR-EI employs an efficient transformer for explicit, HF feature extraction. • We propose LCF that can obtain accurate, implicit HF information. • We propose AFFM to counter the information loss issue. • DSR-EI outperforms other SoTA methods. [ABSTRACT FROM AUTHOR]

VIDET is a research project active at the University of Bologna and aimed at the development of a mobility aid for the visually impaired. VIDET''s approach consists in the conversion of depth data gathered through a stereo-vision system into a 3D model perceivable by the user by means of a wire-actuated haptic interface. In this paper we describe VIDET''s PC-based, real-time, stereo-vision system. As for system''s description, we outline the structure of the stereo-matching algorithm and address in more detail the optimization strategies that lead us to a fast PC-based implementation. These involve massive reduction of redundant calculations and use of the parallel multimedia instructions available in current general-purpose microprocessors. We provide experimental results showing that the system is capable of recovering correctly the 3D structure of the observed scene and allows for prompt perception of the depth changes generated by moving objects. We also report execution time measurements and compare our stereo system with the PC-based systems from SRI and Point Grey Research. [Copyright &y& Elsevier]

Depth sensing is of paramount importance for countless applications and stereo represents a popular, effective and cheap solution for this purpose. As highlighted by recent works concerned with stereo, uncertainty estimation can be a powerful cue to improve accuracy in stereo. Most confidence measures rely on features, mainly extracted from the cost volume, fed to a random forest or a convolutional neural network trained to estimate match uncertainty. In contrast, we propose a novel strategy for confidence estimation based on features computed in the disparity domain, making our proposal suited for any stereo system including COTS devices, and in constant time. We exhaustively assess the performance of our proposals, referred to as O1 and O2, on KITTI and Middlebury datasets with three popular and different stereo algorithms (CENSUS, MC-CNN and SGM), as well as a deep stereo network (PSM-Net). We also evaluate how well confidence measures generalize to different environments/datasets. • We estimate confidence relying only on the reference disparity map. • A random forest learns to estimate confidence on features from the disparity domain. • A random forest learns to estimate confidence on features from the disparity domain. • A smarter SGM variant is proposed leveraging on the learned confidence measure. [ABSTRACT FROM AUTHOR]