Recent advancements in self-driving cars, robotics, and remote sensing have widened the
range of applications for 3D Point Cloud (PC) data. This data format poses several new …

We present a simple yet powerful neural network that implicitly represents and renders 3D
objects and scenes only from 2D observations. The network models 3D geometries as a …

Shape completion, the problem of estimating the complete geometry of objects from partial
observations, lies at the core of many vision and robotics applications. In this work, we …

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 …

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 …

We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D
human reconstruction from a single RGB image. To reduce the ambiguities associated with …

Recovering the 3D representation of an object from single-view or multi-view RGB images
by deep neural networks has attracted increasing attention in the past few years. Several …

We present SP-GAN, a new unsupervised sphere-guided generative model for direct
synthesis of 3D shapes in the form of point clouds. Compared with existing models, SP-GAN …

Recovering the 3D shape of an object from single or multiple images with deep neural
networks has been attracting increasing attention in the past few years. Mainstream works …

In this paper, we study the problem of 3D scene geometry decomposition and manipulation
from 2D views. By leveraging the recent implicit neural representation techniques …