Deep learning techniques for point cloud data have demonstrated great potentials in solving
classical problems in 3D computer vision such as 3D object classification and segmentation …

Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding
algorithms. However, existing datasets still cover only a limited number of views or a …

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 …

We present an approach to semantic scene analysis using deep convolutional networks.
Our approach is based on tangent convolutions-a new construction for convolutional …

Deep learning with 3D data such as reconstructed point clouds and CAD models has
received great research interests recently. However, the capability of using point clouds with …

Semantic segmentation of 3D meshes is an important problem for 3D scene understanding.
In this paper we revisit the classic multiview representation of 3D meshes and study several …

Scene graphs are a compact and explicit representation successfully used in a variety of 2D
scene understanding tasks. This work proposes a method to build up semantic scene …

We present 3DMV, a novel method for 3D semantic scene segmentation of RGB-D scans
using a joint 3D-multi-view prediction network. In contrast to existing methods that either use …

We present an optimization-based approach that automatically adapts Mixed Reality (MR)
interfaces to different physical environments. Current MR layouts, including the position and …

Automatic estimation of salient object regions across images, without any prior assumption
or knowledge of the contents of the corresponding scenes, enhances many computer vision …