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Found 8 papers, 2 papers with code
WeatherProof: Leveraging Language Guidance for Semantic Segmentation in Adverse Weather
We propose a method to infer semantic segmentation maps from images captured under adverse weather conditions.
GT-Rain Single Image Deraining Challenge Report
This report reviews the results of the GT-Rain challenge on single image deraining at the UG2+ workshop at CVPR 2023.
WeatherProof: A Paired-Dataset Approach to Semantic Segmentation in Adverse Weather
To this end, we create the WeatherProof Dataset, the first semantic segmentation dataset with accurate clear and adverse weather image pairs, which not only enables our new training paradigm, but also improves the evaluation of the performance gap between clear and degraded segmentation.
Enhancing Diffusion Models with 3D Perspective Geometry Constraints
We show that outputs of models trained with this constraint both appear more realistic and improve performance of downstream models trained on generated images.
WeatherStream: Light Transport Automation of Single Image Deweathering
We introduce a pipeline that uses the power of light-transport physics and a model trained on a small, initial seed dataset to reject approximately 99. 6% of unwanted scenes.
Depth Estimation From Camera Image and mmWave Radar Point Cloud
To fuse radar depth with an image, we propose a gated fusion scheme that accounts for the confidence scores of the correspondence so that we selectively combine radar and camera embeddings to yield a dense depth map.
Not Just Streaks: Towards Ground Truth for Single Image Deraining
We propose a large-scale dataset of real-world rainy and clean image pairs and a method to remove degradations, induced by rain streaks and rain accumulation, from the image.
Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning
Accurately predicting the dynamics of robotic systems is crucial for model-based control and reinforcement learning.
Model-based Reinforcement Learning
reinforcement-learning
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