Semantic segmentation, also called scene labeling, refers to the process of assigning a
semantic label (eg car, people, and road) to each pixel of an image. It is an essential data …

Optimization techniques based on graph cuts have become a standard tool for many vision
applications. These techniques allow to minimize efficiently certain energy functions …

Semantic scene understanding is important for various applications. In particular, self-driving
cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light …

We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D SLAM
system that goes beyond traditional systems which output a purely geometric map of a static …

Semantic segmentation of 3D point clouds is a challenging problem with numerous real-
world applications. While deep learning has revolutionized the field of image semantic …

We propose a new method to quickly and accurately predict 3D positions of body joints from
a single depth image, using no temporal information. We take an object recognition …

An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …

We propose a new method to quickly and accurately predict human pose---the 3D positions
of body joints---from a single depth image, without depending on information from preceding …

The depth information of RGB-D sensors has greatly simplified some common challenges in
computer vision and enabled breakthroughs for several tasks. In this paper, we propose to …

In this work we address the task of the contextual classification of an airborne LiDAR point
cloud. For that purpose, we integrate a Random Forest classifier into a Conditional Random …