IEEE Trans Pattern Anal Mach Intell - A Two-Stage Framework for 3D Face Reconstruction From RGBD Images.

Tópicos

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Resumo

This paper proposes a new approach for 3D face reconstruction with RGBD images from an inexpensive commodity sensor. The challenges we face are: 1) substantial random noise and corruption are present in low-resolution depth maps; and 2) there is high degree of variability in pose and face expression. We develop a novel two-stage algorithm that effectively maps low-quality depth maps to realistic face models. Each stage is targeted toward a certain type of noise. The first stage extracts sparse errors from depth patches through the data-driven local sparse coding, while the second stage smooths noise on the boundaries between patches and reconstructs the global shape by combining local shapes using our template-based surface refinement. Our approach does not require any markers or user interaction. We perform quantitative and qualitative evaluations on both synthetic and real test sets. Experimental results show that the proposed approach is able to produce high-resolution 3D face models with high accuracy, even if inputs are of low quality, and have large variations in viewpoint and face expression.

Resumo Limpo

paper propos new approach d face reconstruct rgbd imag inexpens commod sensor challeng face substanti random nois corrupt present lowresolut depth map high degre variabl pose face express develop novel twostag algorithm effect map lowqual depth map realist face model stage target toward certain type nois first stage extract spars error depth patch datadriven local spars code second stage smooth nois boundari patch reconstruct global shape combin local shape use templatebas surfac refin approach requir marker user interact perform quantit qualit evalu synthet real test set experiment result show propos approach abl produc highresolut d face model high accuraci even input low qualiti larg variat viewpoint face express

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