prof. Jana Košecká
Semantic Understanding for Robot Perception
On 2017-10-30 16:00 at G205
Advancements in robotic navigation, mapping, object search and recognition rest
to a large extent on robust, efficient and scalable semantic understanding of 
the surrounding environment. In recent years we have developed several approach
es for capturing geometry and semantics of environment from video, RGB-D data, 
or just simply a single RGB image, focusing on indoors and outdoors

relevant for robotics applications.
I will demonstrate our work on detailed semantic parsing and 3D structure
using deep convolutional neural networks (CNNs) and object detection and object

pose recovery from single RGB image. The applicability of the presented
for autonomous driving, service robotics, mapping and augmented reality 
applications will be discussed.
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