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20192024
most citedWeather Influence and Classification with Automotive Lidar Sensors

225 citations · 738 across the 12 of their papers we have counts for

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Showing 2019Show all

8 papers · 1 filter

cs.CV2019★ 171 cited

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…

eess.IV2019★ 71 cited

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…

eess.IV2019★ 11 cited

Learning Super-resolved Depth from Active Gated Imaging

Tobias Gruber, Mariia Kokhova, Werner Ritter +2

Environment perception for autonomous driving is doomed by the trade-off between range-accuracy and resolution: current sensors that deliver very precise depth information are usua…

cs.CV2019★ 148 cited

Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar

Nicolas Scheiner, Florian Kraus, Fangyin Wei +8

Conventional sensor systems record information about directly visible objects, whereas occluded scene components are considered lost in the measurement process. Non-line-of-sight (…

cs.CV2019★ 225 cited

Weather Influence and Classification with Automotive Lidar Sensors

Robin Heinzler, Philipp Schindler, Jürgen Seekircher +2

Lidar sensors are often used in mobile robots and autonomous vehicles to complement camera, radar and ultrasonic sensors for environment perception. Typically, perception algorithm…

cs.CV2019★ 24 cited

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…

cs.CV2019★ 1 cited

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…

cs.CV2019

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…