Robots operating in the real world are expected to detect, classify, segment, and estimate
the pose of objects to accomplish their task. Modern approaches using deep learning not …

Developing robot perception systems for recognizing objects in the real world requires
computer vision algorithms to be carefully scrutinized with respect to the expected operating …

We present NeuralLabeling, a labeling approach and toolset for annotating a scene using
either bounding boxes or meshes and generating segmentation masks, affordance maps …

Recent advancements in deep learning techniques have accelerated the growth of robotic
vision systems. One way this technology can be applied is to use a mobile robot to …

Visual perception tasks often require vast amounts of labelled data, including 3D poses and
image space segmen-tation masks. The process of creating such training data sets can …

Learning unknown objects in the environment is important for detection and manipulation
tasks. Prior to learning the unknown objects the ground-truth labels have to be provided. The …

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 …

Deep neural network (DNN) architectures have been shown to outperform traditional
pipelines for object segmentation and pose estimation using RGBD data, but the …

General scene understanding for robotics requires flexible semantic representation, so that
novel objects and structures which may not have been known at training time can be …

Inexpensive RGB-D cameras that give an RGB image together with depth data have
become widely available. We use this data to build 3D point clouds of a full scene. In this …