Ripe with possibilities offered by deep-learning techniques and useful in applications
related to remote sensing, computer vision, and robotics, 3D point cloud semantic …

The advent of affordable consumer grade RGB‐D cameras has brought about a profound
advancement of visual scene reconstruction methods. Both computer graphics and …

We propose the first fast and certifiable algorithm for the registration of two sets of three-
dimensional (3-D) points in the presence of large amounts of outlier correspondences. A …

Abstract We introduce 3D-FRONT (3D Furnished Rooms with layOuts and semaNTics), a
new, large-scale, and compre-hensive repository of synthetic indoor scenes highlighted by …

We propose a novel deep learning-based framework to tackle the challenge of semantic
segmentation of large-scale point clouds of millions of points. We argue that the organization …

3D semantic scene labeling is fundamental to agents operating in the real world. In
particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent …

We introduce the task of open-vocabulary 3D instance segmentation. Traditional
approaches for 3D instance segmentation largely rely on existing 3D annotated datasets …

We present a dataset of large-scale indoor spaces that provides a variety of mutually
registered modalities from 2D, 2.5 D and 3D domains, with instance-level semantic and …

Semantic segmentation is a challenging task in computer vision systems. A lot of methods
have been developed to tackle this problem ranging from autonomous vehicles, human …

We introduce a neural architecture for navigation in novel environments. Our proposed
architecture learns to map from first-person views and plans a sequence of actions towards …