Details

Autor(en) / Beteiligte

• Dong, Weisheng
• Shi, Guangming
• Li, Xin
• Peng, Kefan
• Wu, Jinjian
• Guo, Zhenhua

Titel

Color-Guided Depth Recovery via Joint Local Structural and Nonlocal Low-Rank Regularization

Ist Teil von

• IEEE transactions on multimedia, 2017-02, Vol.19 (2), p.293-301

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2017

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• High-quality depth recovery from RGB-D data has received increasingly more attention in recent years due to their wide applications from depth-based image rendering to three-dimensional imaging and video. Sharp contrast between high-quality color images and low-quality depth maps presents severe challenges to the development of color-guided depth recovery techniques. Previous works have emphasized either locally varying characteristics of color-depth dependence or nonlocal similarities around the discontinuities of the scene geometry. Therefore, it is desirable to exploit both local and nonlocal structural constraints for optimizing the performance of color-guided depth recovery. In this work, we propose a unified variational approach via joint local and nonlocal regularization. The local regularization term consists of two complementary parts-one characterizing the color-depth dependence in the gradient domain and the other in the spatial domain; nonlocal regularization involves a low-rank constraint suitable for large-scale depth discontinuities. Extensive experimental results are reported to show that our approach outperforms several existing state-of-the-art depth recovery methods on both synthetic and real-world data sets.