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Werner Ritter

6 accepted papers

2025

A Multi-Modal Benchmark for Long-Range Depth Evaluation in Adverse Weather Conditions

Stefanie Walz, Andrea Ramazzina, Dominik Scheuble, Samuel Brucker, Alexander Zuber, Werner Ritter +2
IROS 2025

Depth estimation is a cornerstone computer vision application that is critical for scene understanding and autonomous driving. In real-world scenarios, achieving reliable depth perception under adverse weather—e.g. in fog and rain—is crucial to ensure safety and system robustness. However, quantitat

Cited by 0Source ↗Scholar ↗
2022

Gated2Gated: Self-Supervised Depth Estimation From Gated Images

Amanpreet Walia, Stefanie Walz, Mario Bijelic, Fahim Mannan, Frank Julca-Aguilar, Michael Langer +2
CVPR 2022oral

Gated cameras hold promise as an alternative to scanning LiDAR sensors with high-resolution 3D depth that is robust to back-scatter in fog, snow, and rain. Instead of sequentially scanning a scene and directly recording depth via the photon time-of-flight, as in pulsed LiDAR sensors, gated imagers e…

Cited by 20PDF ↗code ↗Scholar ↗
2021

The Radar Ghost Dataset – An Evaluation of Ghost Objects in Automotive Radar Data

Florian Kraus, Nicolas Scheiner, Werner Ritter, Klaus Dietmayer
IROS 2021poster

Radar sensors have a long tradition in advanced driver assistance systems (ADAS) and also play a major role in current concepts for autonomous vehicles. Their importance is reasoned by their high robustness against meteorological effects, such as rain, snow, or fog, and the radar’s ability to measur…

Cited by 23Source ↗code ↗Scholar ↗
2021

ZeroScatter: Domain Transfer for Long Distance Imaging and Vision Through Scattering Media

Zheng Shi, Ethan Tseng, Mario Bijelic, Werner Ritter, Felix Heide
CVPR 2021poster

Adverse weather conditions, including snow, rain, and fog, pose a major challenge for both human and computer vision. Handling these environmental conditions is essential for safe decision making, especially in autonomous vehicles, robotics, and drones. Most of today's supervised imaging and vision…

Cited by 15PDF ↗code ↗Scholar ↗
2020

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

Nicolas Scheiner, Florian Kraus, Fangyin Wei, Buu Phan, Fahim Mannan, Nils Appenrodt +5
CVPR 2020poster

Conventional sensor systems record information about directly visible objects, whereas occluded scene components are considered lost in the measurement process. Non-line-of-sight (NLOS) methods try to recover such hidden objects from their indirect reflections - faint signal components, traditionall…

Cited by 163PDF ↗code ↗Scholar ↗
2020

Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather

Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer +1
CVPR 2020poster

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 making on these inputs. While existing methods exploit redundant information in good environmental conditions, they fai…

Cited by 577PDF ↗code ↗Scholar ↗