English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The rapid development of 3D technology and computer vision applications havemotivated a thrust of methodologies for depth acquisition and estimation.However, most existing hardware and software methods have limited performancedue to poor depth precision, low resolution and high computational cost. Inthis paper, we present a computationally efficient method to recover densedepth maps from sparse measurements. We make three contributions. First, weprovide empirical evidence that depth maps can be encoded much more sparselythan natural images by using common dictionaries such as wavelets andcontourlets. We also show that a combined wavelet-contourlet dictionaryachieves better performance than using either dictionary alone. Second, wepropose an alternating direction method of multipliers (ADMM) to achieve fastreconstruction. A multi-scale warm start procedure is proposed to speed up theconvergence. Third, we propose a two-stage randomized sampling scheme tooptimally choose the sampling locations, thus maximizing the reconstructionperformance for any given sampling budget. Experimental results show that theproposed method produces high quality dense depth estimates, and is robust tonoisy measurements. Applications to real data in stereo matching aredemonstrated.

Depth Reconstruction from Sparse Samples: Representation, Algorithm, and Sampling