Hallucinating

Algorithm

Linguistics

Philosophy

Bayesian probability

Computer science

Rendering (computer graphics)

Thermodynamics

Physics

Finite element method

Mesh generation

Pattern recognition (psychology)

Politics

Political science

Feature (linguistics)

Artificial intelligence

Representation (politics)

Morphing

Prior probability

Artificial neural network

Computer vision

Convolution (computer science)

IEEE Transactions on Pattern Analysis and Machine Intelligence

We study the problem of shape generation in 3D mesh representation from a small number of color images with or without camera poses. While many previous works learn to hallucinate the shape directly from priors, we adopt to further improve the shape quality by leveraging cross-view information with a graph convolution network. Instead of building a direct mapping function from images to 3D shape, our model learns to predict series of deformations to improve a coarse shape iteratively. Inspired by traditional multiple view geometry methods, our network samples nearby area around the initial mesh's vertex locations and reasons an optimal deformation using perceptual feature statistics built from multiple input images. Extensive experiments show that our model produces accurate 3D shapes that are not only visually plausible from the input perspectives, but also well aligned to arbitrary viewpoints. With the help of physically driven architecture, our model also exhibits generalization capability across different semantic categories, and the number of input images. Model analysis experiments show that our model is robust to the quality of the initial mesh and the error of camera pose, and can be combined with a differentiable renderer for test-time optimization.

Pixel2Mesh++: 3D Mesh Generation and Refinement From Multi-View Images