7 papers · 1 filter
Evidential Neural Radiance Fields
Ruxiao Duan, Alex Wong
Understanding sources of uncertainty is fundamental to trustworthy three-dimensional scene modeling. While recent advances in neural radiance fields (NeRFs) achieve impressive accu…
WorldBench: Benchmarking Physical Understanding of World Models by Isolating Physics Concepts
Rishi Upadhyay, Howard Zhang, Jim Solomon +5
Recent advances in generative foundational models, often termed "world models," have propelled interest in applying them to critical tasks like robotic planning and autonomous syst…
All-day Depth Completion
Vadim Ezhov, Hyoungseob Park, Zhaoyang Zhang +7
We propose a method for depth estimation under different illumination conditions, i.e., day and night time. As photometry is uninformative in regions under low-illumination, we tac…
GT-Rain Single Image Deraining Challenge Report
Howard Zhang, Yunhao Ba, Ethan Yang +20
This report reviews the results of the GT-Rain challenge on single image deraining at the UG2+ workshop at CVPR 2023. The aim of this competition is to study the rainy weather phen…
WeatherProof: Leveraging Language Guidance for Semantic Segmentation in Adverse Weather
Blake Gella, Howard Zhang, Rishi Upadhyay +7
We propose a method to infer semantic segmentation maps from images captured under adverse weather conditions. We begin by examining existing models on images degraded by weather c…
WeatherProof: A Paired-Dataset Approach to Semantic Segmentation in Adverse Weather
Blake Gella, Howard Zhang, Rishi Upadhyay +5
The introduction of large, foundational models to computer vision has led to drastically improved performance on the task of semantic segmentation. However, these existing methods…