The reconstruction of 3D object from a single image is an important task in the field of
computer vision. In recent years, 3D reconstruction of single image using deep learning …

We advocate the use of implicit fields for learning generative models of shapes and
introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving …

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 …

We introduce a method for learning to generate the surface of 3D shapes. Our approach
represents a 3D shape as a collection of parametric surface elements and, in contrast to …

Representing shapes as level sets of neural networks has been recently proved to be useful
for different shape analysis and reconstruction tasks. So far, such representations were …

Researchers have achieved great success in dealing with 2D images using deep learning.
In recent years, 3D computer vision and geometry deep learning have gained ever more …

Recently, neural networks have been used as implicit representations for surface
reconstruction, modelling, learning, and generation. So far, training neural networks to be …

We propose a straightforward method that simultaneously reconstructs the 3D facial
structure and provides dense alignment. To achieve this, we design a 2D representation …

Reconstructing 3D shapes from single-view images has been a long-standing research
problem. In this paper, we present DISN, a Deep Implicit Surface Net-work which can …

We present a learning framework for recovering the 3D shape, camera, and texture of an
object from a single image. The shape is represented as a deformable 3D mesh model of an …