Search Results for author: Dominik Scheuble

Found 4 papers, 1 papers with code

ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering

no code implementations ICCV 2023 Andrea Ramazzina, Mario Bijelic, Stefanie Walz, Alessandro Sanvito, Dominik Scheuble, Felix Heide

With data as bottleneck and most of today's training data relying on good weather conditions with inclement weather as outlier, we rely on an inverse rendering approach to reconstruct the scene content.

Autonomous Vehicles Inverse Rendering +1

Survey on LiDAR Perception in Adverse Weather Conditions

no code implementations13 Apr 2023 Mariella Dreissig, Dominik Scheuble, Florian Piewak, Joschka Boedecker

The active LiDAR sensor is able to create an accurate 3D representation of a scene, making it a valuable addition for environment perception for autonomous vehicles.

Autonomous Vehicles Denoising +1

LiDAR-in-the-Loop Hyperparameter Optimization

no code implementations CVPR 2023 Félix Goudreault, Dominik Scheuble, Mario Bijelic, Nicolas Robidoux, Felix Heide

The resulting point clouds output by these DSPs are input to downstream 3D vision models -- both, in the form of training datasets or as input at inference time.

3D Object Detection Hyperparameter Optimization +1

Simulating Road Spray Effects in Automotive Lidar Sensor Models

1 code implementation16 Dec 2022 Clemens Linnhoff, Dominik Scheuble, Mario Bijelic, Lukas Elster, Philipp Rosenberger, Werner Ritter, Dengxin Dai, Hermann Winner

The model conforms to the Open Simulation Interface (OSI) standard and is based on the formation of detection clusters within a spray plume.

object-detection Object Detection

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