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 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 …

Generation of 3D data by deep neural network has been attracting increasing attention in
the research community. The majority of extant works resort to regular representations such …

We present OctNet, a representation for deep learning with sparse 3D data. In contrast to
existing models, our representation enables 3D convolutional networks which are both deep …

Motivated by the limitations of existing multi-view stereo benchmarks, we present a novel
dataset for this task. Towards this goal, we recorded a variety of indoor and outdoor scenes …

What is a good vector representation of an object? We believe that it should be generative in
3D, in the sense that it can produce new 3D objects; as well as be predictable from 2D, in …

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 …

The area of surface reconstruction has seen substantial progress in the past two decades.
The traditional problem addressed by surface reconstruction is to recover the digital …

This paper introduces a deep architecture for segmenting 3D objects into their labeled
semantic parts. Our architecture combines image-based Fully Convolutional Networks …

We present a learning framework for abstracting complex shapes by learning to assemble
objects using 3D volumetric primitives. In addition to generating simple and geometrically …