171 citations · 283 across the 29 of their papers we have counts for
5 papers · 1 filter
A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?
Mario Bijelic, Tobias Gruber, Werner Ritter
Autonomous driving at level five does not only means self-driving in the sunshine. Adverse weather is especially critical because fog, rain, and snow degrade the perception of the…
Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving
Mario Bijelic, Tobias Gruber, Werner Ritter
Adverse weather conditions are very challenging for autonomous driving because most of the state-of-the-art sensors stop working reliably under these conditions. In order to develo…
Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios
Tobias Gruber, Mario Bijelic, Felix Heide +2
This work introduces an evaluation benchmark for depth estimation and completion using high-resolution depth measurements with angular resolution of up to 25" (arcsecond), akin to…
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather
Mario Bijelic, Tobias Gruber, Fahim Mannan +4
The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision…
Gated2Depth: Real-time Dense Lidar from Gated Images
Tobias Gruber, Frank Julca-Aguilar, Mario Bijelic +3
We present an imaging framework which converts three images from a gated camera into high-resolution depth maps with depth accuracy comparable to pulsed lidar measurements. Existin…