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 …

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 …

3D reconstruction is a longstanding ill-posed problem, which has been explored for decades
by the computer vision, computer graphics, and machine learning communities. Since 2015 …

Convolutional networks for single-view object reconstruction have shown impressive
performance and have become a popular subject of research. All existing techniques are …

Polygonal meshes are ubiquitous in the digital 3D domain, yet they have only played a
minor role in the deep learning revolution. Leading methods for learning generative models …

We study the problem of shape generation in 3D mesh representation from a few color
images with known camera poses. While many previous works learn to hallucinate the …

Point set is a flexible and lightweight representation widely used for 3D deep learning.
However, their discrete nature prevents them from representing continuous and fine …

Abstracting complex 3D shapes with parsimonious part-based representations has been a
long standing goal in computer vision. This paper presents a learning-based solution to this …

We introduce PQ-NET, a deep neural network which represents and generates 3D shapes
via sequential part assembly. The input to our network is a 3D shape segmented into parts …

Impressive progress in generative models and implicit representations gave rise to methods
that can generate 3D shapes of high quality. However, being able to locally control and edit …