Pixel2Mesh swMATH ID: 31205 Software Authors: Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, Yu-Gang Jiang Description: Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images. We propose an end-to-end deep learning architecture that produces a 3D shape in triangular mesh from a single color image. Limited by the nature of deep neural network, previous methods usually represent a 3D shape in volume or point cloud, and it is non-trivial to convert them to the more ready-to-use mesh model. Unlike the existing methods, our network represents 3D mesh in a graph-based convolutional neural network and produces correct geometry by progressively deforming an ellipsoid, leveraging perceptual features extracted from the input image. We adopt a coarse-to-fine strategy to make the whole deformation procedure stable, and define various of mesh related losses to capture properties of different levels to guarantee visually appealing and physically accurate 3D geometry. Extensive experiments show that our method not only qualitatively produces mesh model with better details, but also achieves higher 3D shape estimation accuracy compared to the state-of-the-art. Homepage: https://arxiv.org/abs/1804.01654 Source Code: https://github.com/nywang16/Pixel2Mesh Related Software: PointNet; ShapeNet; 3D-R2N2; PyTorch; DeepSDF; Adam; MNIST; SynSin; PIFu; ImageNet; DensePose; SMPL; NeRF; Kaolin; TensorFlow; OpenDR; SoftRas; pix2pix; CycleGAN; CamNet Cited in: 4 Publications all top 5 Cited by 7 Authors 1 Chen, Xuejin 1 Ferrari, Vittorio 1 Henderson, Paul 1 Hu, Siyu 1 Lian, Zhouhui 1 Sun, Xiao 1 Szeliski, Richard Cited in 3 Serials 2 Computer Aided Geometric Design 1 International Journal of Computer Vision 1 Texts in Computer Science Cited in 3 Fields 2 Numerical analysis (65-XX) 2 Computer science (68-XX) 1 Information and communication theory, circuits (94-XX) Citations by Year