GTAV-NightRain: Photometric Realistic Large-scale Dataset for Night-time
Rain Streak Removal
- URL: http://arxiv.org/abs/2210.04708v1
- Date: Mon, 10 Oct 2022 14:08:09 GMT
- Title: GTAV-NightRain: Photometric Realistic Large-scale Dataset for Night-time
Rain Streak Removal
- Authors: Fan Zhang, Shaodi You, Yu Li, Ying Fu
- Abstract summary: Rain is transparent, which reflects and refracts light in the scene to the camera.
In existing rain streak removal datasets, although density, scale, direction and intensity have been considered, transparency is not fully taken into account.
This paper proposes GTAV-NightRain dataset, which is a large-scale synthetic night-time rain streak removal dataset.
- Score: 30.93624632770902
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Rethinking Rainy 3D Scene Reconstruction via Perspective Transforming and Brightness Tuning [8.552623283033695]
Existing datasets often overlook two critical characteristics of real rainy 3D scenes.<n>We construct a new dataset named OmniRain3D that incorporates perspective heterogeneity and brightness dynamicity.<n>We propose an end-to-end reconstruction framework named REVR-GSNet, achieving high-fidelity reconstruction of clean 3D scenes from rain-degraded inputs.
arXiv Detail & Related papers (2025-11-10T05:57:55Z) - Rethinking Nighttime Image Deraining via Learnable Color Space Transformation [38.0322908418521]
We develop a new high-quality benchmark, HQ-NightRain, which offers higher harmony and realism compared to existing datasets.<n>We also develop an effective Color Space Transformation Network (CST-Net) for better removing complex rain from nighttime scenes.
arXiv Detail & Related papers (2025-10-20T11:28:43Z) - NDLPNet: A Location-Aware Nighttime Deraining Network and a Real-World Benchmark Dataset [8.582528726118023]
Rain streak artifacts hamper the performance of nighttime surveillance and autonomous navigation.<n>We propose a novel Nighttime Deraining Location-enhanced Perceptual Network (NDLPNet)<n>NDLPNet captures the spatial positional information and density distribution of rain streaks in low-light environments.
arXiv Detail & Related papers (2025-09-17T07:24:47Z) - REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-raining [0.5668912212306543]
We release a new, large-scale, multi-modal emulated rain dataset, REHEARSE-3D, to promote research advancements in 3D point cloud de-raining.
First, it is the largest point-wise annotated dataset, and second, it is the only one with high-resolution LiDAR data enriched with 4D Radar point clouds.
We benchmark raindrop detection and removal in fused LiDAR and 4D Radar point clouds.
arXiv Detail & Related papers (2025-04-30T14:43:38Z) - NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results [173.5963741512905]
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images.
This challenge received a wide range of impressive solutions, which are developed and evaluated using our collected real-world Raindrop Clarity dataset.
The primary objective of this challenge is to establish a new and powerful benchmark for the task of removing raindrops under varying lighting and focus conditions.
arXiv Detail & Related papers (2025-04-17T07:35:35Z) - SpikeDerain: Unveiling Clear Videos from Rainy Sequences Using Color Spike Streams [49.34425133546994]
Restoring clear frames from rainy videos presents a significant challenge due to the rapid motion of rain streaks.
Traditional frame-based visual sensors, which capture scene content synchronously, struggle to capture the fast-moving details of rain accurately.
We propose a Color Spike Stream Deraining Network (SpikeDerain), capable of reconstructing spike streams of dynamic scenes and accurately removing rain streaks.
arXiv Detail & Related papers (2025-03-26T08:28:28Z) - Sun Off, Lights On: Photorealistic Monocular Nighttime Simulation for Robust Semantic Perception [53.631644875171595]
Nighttime scenes are hard to semantically perceive with learned models and annotate for humans.
Our method, named Sun Off, Lights On (SOLO), is the first to perform nighttime simulation on single images in a photorealistic fashion by operating in 3D.
Not only is the visual quality and photorealism of our nighttime images superior to competing approaches including diffusion models, but the former images are also proven more beneficial for semantic nighttime segmentation in day-to-night adaptation.
arXiv Detail & Related papers (2024-07-29T18:00:09Z) - Raindrop Clarity: A Dual-Focused Dataset for Day and Night Raindrop Removal [16.50219011463268]
Raindrop Clarity comprises 15,186 high-quality pairs/triplets of images with raindrops and the corresponding clear background images.
There are 5,442 daytime raindrop images and 9,744 nighttime raindrop images.
Our dataset will enable the community to explore background-focused and raindrop-focused images.
arXiv Detail & Related papers (2024-07-24T02:48:30Z) - GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction [11.610543327501995]
High Dynamic Range (i.e., images and videos) has a broad range of applications.
High Dynamic Range (i.e., images and videos) has a broad range of applications.
The challenging task of reconstructing visually accurate HDR images from their Low Dynamic Range (LDR) counterparts is gaining attention in the vision research community.
arXiv Detail & Related papers (2024-03-26T16:24:42Z) - NiteDR: Nighttime Image De-Raining with Cross-View Sensor Cooperative Learning for Dynamic Driving Scenes [49.92839157944134]
In nighttime driving scenes, insufficient and uneven lighting shrouds the scenes in darkness, resulting degradation of image quality and visibility.
We develop an image de-raining framework tailored for rainy nighttime driving scenes.
It aims to remove rain artifacts, enrich scene representation, and restore useful information.
arXiv Detail & Related papers (2024-02-28T09:02:33Z) - Dual Degradation Representation for Joint Deraining and Low-Light Enhancement in the Dark [57.85378202032541]
Rain in the dark poses a significant challenge to deploying real-world applications such as autonomous driving, surveillance systems, and night photography.
Existing low-light enhancement or deraining methods struggle to brighten low-light conditions and remove rain simultaneously.
We introduce an end-to-end model called L$2$RIRNet, designed to manage both low-light enhancement and deraining in real-world settings.
arXiv Detail & Related papers (2023-05-06T10:17:42Z) - Not Just Streaks: Towards Ground Truth for Single Image Deraining [42.15398478201746]
We propose a large-scale dataset of real-world rainy and clean image pairs.
We propose a deep neural network that reconstructs the underlying scene by minimizing a rain-robust loss between rainy and clean images.
arXiv Detail & Related papers (2022-06-22T00:10:06Z) - Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal [21.316673824040752]
In real-life scenarios, rain and haze, two often co-occurring common weather phenomena, can greatly degrade the clarity and quality of scene images.
We propose a novel deep neural network, named Asymmetric Dual-decoder U-Net (ADU-Net), to address the aforementioned challenge.
The ADU-Net produces both the contamination residual and the scene residual to efficiently remove the rain and haze while preserving the fidelity of the scene information.
arXiv Detail & Related papers (2022-06-14T12:50:41Z) - Toward Real-world Single Image Deraining: A New Benchmark and Beyond [79.5893880599847]
Single image deraining (SID) in real scenarios attracts increasing attention in recent years.
Previous real datasets suffer from low-resolution images, homogeneous rain streaks, limited background variation, and even misalignment of image pairs.
We establish a new high-quality dataset named RealRain-1k, consisting of $1,120$ high-resolution paired clean and rainy images with low- and high-density rain streaks, respectively.
arXiv Detail & Related papers (2022-06-11T12:26:59Z) - MBA-RainGAN: Multi-branch Attention Generative Adversarial Network for
Mixture of Rain Removal from Single Images [24.60495609529114]
Rain severely hampers the visibility of scene objects when images are captured through glass in heavily rainy days.
We observe three intriguing phenomenons that, 1) rain is a mixture of raindrops, rain streaks and rainy haze; 2) the depth from the camera determines the degrees of object visibility; and 3) raindrops on the glass randomly affect the object visibility of the whole image space.
arXiv Detail & Related papers (2020-05-21T11:44:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.