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20182024
most cited3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods

10 citations · 23 across the 22 of their papers we have counts for

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Showing 2020Show all

7 papers · 1 filter

cs.RO2020

Deep Inverse Sensor Models as Priors for evidential Occupancy Mapping

Daniel Bauer, Lars Kuhnert, Lutz Eckstein

With the recent boost in autonomous driving, increased attention has been paid on radars as an input for occupancy mapping. Besides their many benefits, the inference of occupied s…

cs.OH20201 cited

6-Layer Model for a Structured Description and Categorization of Urban Traffic and Environment

Maike Scholtes, Lukas Westhofen, Lara Ruth Turner +11

Verification and validation of automated driving functions impose large challenges. Currently, scenario-based approaches are investigated in research and industry, aiming at a redu…

cs.CV2020

Real-Time Point Cloud Fusion of Multi-LiDAR Infrastructure Sensor Setups with Unknown Spatial Location and Orientation

Laurent Kloeker, Christian Kotulla, Lutz Eckstein

The use of infrastructure sensor technology for traffic detection has already been proven several times. However, extrinsic sensor calibration is still a challenge for the operator…

cs.CV2020

High-Precision Digital Traffic Recording with Multi-LiDAR Infrastructure Sensor Setups

Laurent Kloeker, Christian Geller, Amarin Kloeker +1

Large driving datasets are a key component in the current development and safeguarding of automated driving functions. Various methods can be used to collect such driving data reco…

cs.OH2020

Generation of Complex Road Networks Using a Simplified Logical Description for the Validation of Automated Vehicles

Daniel Becker, Fabian Ruß, Christian Geller +1

Simulation is a valuable building block for the verification and validation of automated driving functions (ADF). When simulating urban driving scenarios, simulation maps are one i…

cs.CV20201 cited

A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye View

Lennart Reiher, Bastian Lampe, Lutz Eckstein

Accurate environment perception is essential for automated driving. When using monocular cameras, the distance estimation of elements in the environment poses a major challenge. Di…