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 …

For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-
pixel ground truth labels from real images. We address this challenge by introducing …

A comprehensive semantic understanding of a scene is important for many applications-but
in what space should diverse semantic information (eg, objects, scene categories, material …

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 …

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 …

Several RGB-D datasets have been publicized over the past few years for facilitating
research in computer vision and robotics. However, the lack of comprehensive and fine …

Due to constraints in manufacturing and construction, buildings and many of the manmade
objects within them are often rectangular and composed of planar parts. Detection and …

Online 3D semantic segmentation, which aims to perform real-time 3D scene reconstruction
along with semantic segmentation, is an important but challenging topic. A key challenge is …

A neural field trained with self-supervision to efficiently represent the geometry and colour of
a 3D scene tends to automatically decompose it into coherent and accurate object-like …

We introduce BuildingNet:(a) a large-scale dataset of 3D building models whose exteriors
are consistently labeled, and (b) a graph neural network that labels building meshes by …