GTAV-NightRain: Photometric Realistic Large-scale Dataset for Night-time Rain Streak Removal

TL;DR

This paper introduces GTAV-NightRain, a large-scale, photometrically realistic synthetic dataset for night-time rain streak removal, addressing the limitations of existing datasets by modeling rain-light interactions using GTA V.

Contribution

The paper presents a novel large-scale night-time rain dataset generated via GTA V, capturing realistic rain-light interactions to reduce domain bias in rain removal research.

Findings

The dataset contains 12,860 HD rainy images and 1,286 ground truths.

Benchmark analysis demonstrates the dataset's effectiveness for training rain removal models.

Photometric realism improves model generalization to real night-time rain scenes.

Abstract

Rain is transparent, which reflects and refracts light in the scene to the camera. In outdoor vision, rain, especially rain streaks degrade visibility and therefore need to be removed. In existing rain streak removal datasets, although density, scale, direction and intensity have been considered, transparency is not fully taken into account. This problem is particularly serious in night scenes, where the appearance of rain largely depends on the interaction with scene illuminations and changes drastically on different positions within the image. This is problematic, because unrealistic dataset causes serious domain bias. In this paper, we propose GTAV-NightRain dataset, which is a large-scale synthetic night-time rain streak removal dataset. Unlike existing datasets, by using 3D computer graphic platform (namely GTA V), we are allowed to infer the three dimensional interaction between rain and illuminations, which insures the photometric realness. Current release of the dataset contains 12,860 HD rainy images and 1,286 corresponding HD ground truth images in diversified night scenes. A systematic benchmark and analysis are provided along with the dataset to inspire further research.