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A Two-Stage Framework for 3D FaceReconstruction from RGBD Images

机译：从RGBD图像重建3D人脸的两阶段框架

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摘要

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.
机译：本文提出了一种使用廉价商品传感器的RGBD图像进行3D人脸重建的新方法。我们面临的挑战是：1）低分辨率深度图中存在大量随机噪声和破坏； 2）姿势和面部表情变化很大。我们开发了一种新颖的两阶段算法，可以有效地将低质量深度图映射到逼真的面部模型。每个阶段都针对某种类型的噪声。第一阶段通过数据驱动的局部稀疏编码从深度斑块中提取稀疏误差，而第二阶段则平滑毛刺之间的边界上的噪声，并通过使用我们基于模板的表面优化技术结合局部形状来重建全局形状。我们的方法不需要任何标记或用户交互。我们对综合和真实测试集进行定量和定性评估。实验结果表明，即使输入的图像质量较差，并且在视点和面部表情方面存在较大差异，该方法仍能够生成高精度的高分辨率3D面部模型。

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