Philipp Jund — accepted AI-conference papers

2022

Scalable Scene Flow From Point Clouds in the Real World

Philipp Jund, Chris Sweeney, Nichola Abdo, Zhifeng Chen, Jonathon Shlens

RA-L 2022

Autonomous vehicles operate in highly dynamic environments necessitating an accurate assessment of which aspects of a scene are moving and where they are moving to. A popular approach to 3D motion estimation, termed scene flow, is to employ 3D point cloud data from consecutive LiDAR scans, although

Cited by 62 Source ↗Scholar ↗

2018

Optimization Beyond the Convolution: Generalizing Spatial Relations with End-to-End Metric Learning

Philipp Jund, Andreas Eitel, Nichola Abdo, Wolfram Burgard

ICRA 2018poster

To operate intelligently in domestic environments, robots require the ability to understand arbitrary spatial relations between objects and to generalize them to objects of varying sizes and shapes. In this work, we present a novel end-to-end approach to generalize spatial relations based on distanc…

Cited by 23 Source ↗code ↗Scholar ↗