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A Non-local Low Rank and Total Variation Approach for Depth Image Estimation

Nguyen, Uyen and Tong, Truong Giang and Thang Hoa, Tat and Ha, Dai Duong and Ha Tang, Van (2021) A Non-local Low Rank and Total Variation Approach for Depth Image Estimation. In: 15th RIVF International Conference on Computing and Communication Technologies, RIVF 2021, 2 December 2021through 4 December 2021, Hanoi.

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Abstract

Accurate depth reconstruction is vital for numerous applications including autonomous vehicles, virtual reality, and robot perception. However, the depth imaging is challenging because of limited hardware operations, resource-constrained limitations, and incomplete data measurements. To address such shortcomings, this paper introduces an imaging model for efficient depth image estimation from incomplete depth pixels using non-local low-rank (NLLR) and total variation (TV) representations. The motivation is that NLLR is used to model global similar structure among depth patches, and the TV is incorporated to capture the correlations among local depth pixels. We reformulate the problem of depth reconstruction as a regularized least squares minimization problem with the non-local LR and TV regularizers. Furthermore, this paper proposes an iterative algorithm using the alternating direction method of multipliers (ADMM) to solve the optimization model, yielding an estimate of the depth map from far reduced data points. Experimental results on benchmark datasets validate the efficiency of the proposed approach. © 2021 IEEE.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculties > Faculty of Information Technology
Identification Number: 10.1109/RIVF51545.2021.9642135
Uncontrolled Keywords: Depth imaging; Low-rank approximation; RGB-D imaging; Sparse reconstruction
URI: http://eprints.lqdtu.edu.vn/id/eprint/10292

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