Deep neural networks (DNNs) have shown remarkable performance improvements on
vision-related tasks such as object detection or image segmentation. Despite their success …

Natural hazards, which have the potential to cause catastrophic damage and loss to
infrastructure, have increased significantly in recent decades. Thus, the construction …

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 …

This paper presents a new approach for 3D shape generation, enabling direct generative
modeling on a continuous implicit representation in wavelet domain. Specifically, we …

We propose a novel, conceptually simple and general framework for instance segmentation
on 3D point clouds. Our method, called 3D-BoNet, follows the simple design philosophy of …

Autonomous driving has attracted remarkable attention from both industry and academia. An
important task is to estimate 3D properties (eg translation, rotation and shape) of a moving or …

Abstract 3D object grounding aims to locate the most relevant target object in a raw point
cloud scene based on a free-form language description. Understanding complex and …

Numerous methods have been proposed for probabilistic generative modelling of 3D
objects. However, none of these is able to produce textured objects, which renders them of …

The field of single-view 3D shape reconstruction and generation using deep learning
techniques has seen rapid growth in the past five years. As the field is reaching a stage of …