Daniel Wolf — accepted AI-conference papers

2020

Multiple Trajectory Prediction with Deep Temporal and Spatial Convolutional Neural Networks

Jan Strohbeck, Vasileios Belagiannis, Johannes Müller, Marcel Schreiber, Martin Herrmann, Daniel Wolf +1

IROS 2020poster

Automated vehicles need to not only perceive their environment, but also predict the possible future behavior of all detected traffic participants in order to safely navigate in complex scenarios and avoid critical situations, ranging from merging on highways to crossing urban intersections. Due to…

Cited by 46 Source ↗Scholar ↗

2018

Multi-View 3D Entangled Forest for Semantic Segmentation and Mapping

Morris Antonello, Daniel Wolf, Johann Prankl, Stefano Ghidoni, Emanuele Menegatti, Markus Vincze

ICRA 2018poster

Applications that provide location related services need to understand the environment in which humans live such that verbal references and human interaction are possible. We formulate this semantic labelling task as the problem of learning the semantic labels from the perceived 3D structure. In thi…

Cited by 17 Source ↗Scholar ↗

2016

Enhancing Semantic Segmentation for Robotics: The Power of 3-D Entangled Forests

Daniel Wolf, Johann Prankl, Markus Vincze

RA-L 2016

We present a novel, fast, and compact method to improve semantic segmentation of three-dimensional (3-D) point clouds, which is able to learn and exploit common contextual relations between observed structures and objects. Introducing 3-D Entangled Forests (3-DEF), we extend the concept of entangled

Cited by 47 Source ↗Scholar ↗

2015

Fast semantic segmentation of 3D point clouds using a dense CRF with learned parameters

Daniel Wolf, Johann Prankl, Markus Vincze

ICRA 2015poster

In this paper, we present an efficient semantic segmentation framework for indoor scenes operating on 3D point clouds. We use the results of a Random Forest Classifier to initialize the unary potentials of a densely interconnected Conditional Random Field, for which we learn the parameters for the p…

Cited by 124 Source ↗Scholar ↗