# Robust and High-Fidelity 3D Face Reconstruction Using Multiple RGB-D Cameras
## 2 Related Work
However, in this section, we focus on applications that use low-cost depth cameras with depth images or RGB-D image pairs as input data to generate the corresponding 3D face models with 3 DMM. Ref. [1] described a variability model for depth images acquired by a depth camera. Specifically, the features detected by the aligned RGB images allow the template model to complete a rough registration with the depth frame. The template is then non-rigidly aligned with the depth frame data. Unfortunately, this method eventually produces results that are biased towards the template and away from the input data. Ref. [18] proposed a novel facial geometry modeling and reflectivity synthesis procedure to reconstruct the complete head in less than half a minute by recording an RGB-D video of the user rotating the head. The method first selects high-quality frame data available for reconstruction through a two-stage frame screening procedure; then, it recovers facial shapes by fitting 3 DMM parameters from multi-view RGB-D data in a learning-based manner. Although this method can recover accurate geometric shapes and synthesize realistic detailed textures, the method of recording depth video is cumbersome for practical applications, and its input data are redundant. Ref. [19] proposed a fine-grained reconstruction network for the problem of fine-grained geometric loss in 3 DMM template reconstruction; they focused on shape modification by distorting the network input and output to UV space and constructing a new, fine-grained 3D face dataset from the RGB-D data. Ref. [20] proposed an efficient face reconstruction and real-time facial expressions based on RGB-D videos for VR interaction. Specifically, the depth image is first captured using an RGB-D camera, and a rough face model is then quickly reconstructed. The user's specific avatar is generated using standard facial model templates and shape morphing techniques. In addition, RGB data is used to track the user's head movements and localize facial features. The facial features are automatically linked to the facial model. Finally, the user's avatar can be driven by the facial features using Laplacian deformation. However, it still cannot eliminate the algorithm robustness problem caused by sample differences. Among all the above methods, the first type of strategy offers real-time processing and high accuracy but the visual performance is generally not good enough; it requires redundant data to generate a complete face and does not perform detailed processing of textures. Meanwhile, the second type of strategy does not require any post-processing step, but usually depends on a large amount of training data and differences in accuracy between different samples due to the fitting principle, which is not conducive to practical applications. Therefore, we hope to combine the advantages of these two types of methods and adopt a classical multi-view method to achieve considerable reconstruction performance while maintaining high accuracy and strong generalization so that 3D face reconstruction can be more widely applied.