A key requirement for leveraging supervised deep learning methods is the availability of
large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very …

Abstract 3D scene modeling has long been a fundamental problem in computer graphics
and computer vision. With the popularity of consumer-level RGB-D cameras, there is a …

This paper focuses on semantic scene completion, a task for producing a complete 3D voxel
representation of volumetric occupancy and semantic labels for a scene from a single-view …

Although RGB-D sensors have enabled major breakthroughs for several vision tasks, such
as 3D reconstruction, we have not attained the same level of success in high-level scene …

In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the complete
3D structure of a given object from a single arbitrary depth view using generative adversarial …

The amount of captured 3D data is continuously increasing, with the democratization of
consumer depth cameras, the development of modern multi‐view stereo capture setups and …

In recent years, real‐time 3D scanning technology has developed significantly and is now
able to capture large environments with considerable accuracy. Unfortunately, the …

Creating high‐level structured 3D models of real‐world indoor scenes from captured data is
a fundamental task which has important applications in many fields. Given the complexity …

With the proliferation of acquisition devices, gathering massive volumes of 3D data is now
easy. Processing such large masses of pointclouds, however, remains a challenge. This is …

We present a robust approach for reconstructing the main architectural structure of complex
indoor environments given a set of cluttered 3D input range scans. Our method uses an …