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https://www.selleckchem.com/products/GDC-0449.html Finally, we obtain similarity index by pooling the local graph significance across all color channels and averaging across all graphs. We evaluate GraphSIM on two large and independent point cloud assessment datasets that involve a wide range of impairments (e.g., re-sampling, compression, and additive noise). GraphSIM provides state-of-the-art performance for all distortions with noticeable gains in predicting the subjective mean opinion score (MOS) in comparison with point-wise distance-based metrics adopted in standardized reference software. Ablation studies further show that GraphSIM can be generalized to various scenarios with consistent performance by adjusting its key modules and parameters. Models and associated materials will be made available at https//njuvision.github.io/GraphSIM or http//smt.sjtu.edu.cn/papers/GraphSIM.We present SfSNet, an end-to-end learning framework for producing an accurate decomposition of an unconstrained human face image into shape, reflectance and illuminance. SfSNet is designed to reflect a physical lambertian rendering model. SfSNet learns from a mixture of labeled synthetic and unlabeled real world images. This allows the network to capture low frequency variations from synthetic and high frequency details from real images through the photometric reconstruction loss. SfSNet consists of a new decomposition architecture with residual blocks that learns a complete separation of albedo and normal. This is used along with the original image to predict lighting. SfSNet produces significantly better quantitative and qualitative results than state-of-the-art methods for inverse rendering and independent normal and illumination estimation. We also introduce a companion network, SfSMesh, that utilizes normals estimated by SfSNet to reconstruct a 3D face mesh. We demonstrate that SfSMesh produces face meshes with greater accuracy than state-of-the-art methods on real world images.Focus

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