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Stefanie Walz

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

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2024

Gated Fields: Learning Scene Reconstruction from Gated Videos

Andrea Ramazzina, Stefanie Walz, Pragyan Dahal, Mario Bijelic, Felix Heide
CVPR 2024poster

Reconstructing outdoor 3D scenes from temporal observations is a challenge that recent work on neural fields has offered a new avenue for. However existing methods that recover scene properties such as geometry appearance or radiance solely from RGB captures often fail when handling poorly-lit or te…

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2023

Gated Stereo: Joint Depth Estimation From Gated and Wide-Baseline Active Stereo Cues

Stefanie Walz, Mario Bijelic, Andrea Ramazzina, Amanpreet Walia, Fahim Mannan, Felix Heide
CVPR 2023highlight

We propose Gated Stereo, a high-resolution and long-range depth estimation technique that operates on active gated stereo images. Using active and high dynamic range passive captures, Gated Stereo exploits multi-view cues alongside time-of-flight intensity cues from active gating. To this end, we pr…

Cited by 8Source ↗PDF ↗Scholar ↗
2023

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

Andrea Ramazzina, Mario Bijelic, Stefanie Walz, Alessandro Sanvito, Dominik Scheuble, Felix Heide
ICCV 2023poster

Vision in adverse weather conditions, whether it be snow, rain, or fog is challenging. In these scenarios, scattering and attenuation severly degrades image quality. Handling such inclement weather conditions, however, is essential to operate autonomous vehicles, drones and robotic applications wher…

Cited by 21PDF ↗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…

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