Image-to-Image Translation

Mila Simulated Floods Dataset is a 1.5 square km virtual world using the Unity3D game engine including urban, suburban and rural areas. The urban environment contains skyscrapers, large buildings, and roads, as well as objects such as traffic items and vehicles. The rural environment consists of a landscape of grassy hills, forests, and mountains, with sparse houses and other buildings such as a church, and no roads. The rural and urban areas make up for 1 square km of our virtual world. The suburban environment is a residential area of 0.5 square km with many individual houses with front yards. To gather the simulated dataset, we captured before and after flood pairs from 2000 viewpoints with the following modalities: - non-flooded RGB image, depth map, segmentation map - flooded RGB image, binary mask of the flooded area, segmentation map The camera was placed about 1.5 above ground, and has a field of view of 120 degree, and the resolution of the images is 1200 x 900. At each viewpoint, we took 10 pictures, by varying slightly the position of the camera in order to augment the dataset.

An experimental and synthetic (simulated) OA raw signals and reconstructed image domain datasets rendered with different experimental parameters and tomographic acquisition geometries. For detailed information, see github.com/berkanlafci/oadat.

The 2007 BP TTI Velocity-Analysis Benchmark dataset was created by Hemang Shah and provided courtesy of BP Exploration Operation Company Limited ("BP"). It was originally used at the velocity-analysis workshop at the 2010 EAGE meeting in Barcelona, Spain.

BeautyFace is a dataset containing 3,000 high-quality face images with a higher resolution of 512*512, covering more recent makeup styles and more diverse face poses, backgrounds, expressions, races, illumination. Each face has annotated parsing map. Source: BeautyREC: Robust, Efficient, and Component-Specific Makeup Transfer Image Source: https://arxiv.org/pdf/2212.05855v1.pdf

This dataset contains synthetic images extracted from the CARLA simulator along with rich information extracted from the deferred rendering pipeline of Unreal Engine 4. The main purpose of this dataset is the training of the state-of-the-art image-to-image translation model proposed by Intel Labs "Enhancing Photorealism Enhancement" (EPE). Translation results derived from the model targeting the characteristics of Cityscapes, KITTI, and Mapillary Vistas are also provided. Computer vision-based models trained on these data are expected to perform better when deployed in the real world.

CPM-Synt-1 is a dataset consisting of 5555 images with synthesis - makeup images with pattern segmentation mask

CPM-Synt-2 is a dataset consisting of 1625 images with synthesis - triplets: makeup, non-makeup, ground-truth.

LISA Gaze is a dataset for driver gaze estimation comprising of 11 long drives, driven by 10 subjects in two different cars. Source: Driver Gaze Zone Estimation using Convolutional Neural Networks: A General Framework and Ablative Analysis

This dataset builds upon the SpaGBOL dataset - a graph-based dataset covering numerous cities across the globe for the purpose of structured city-scale Cross-View Geo-Localisation (CVGL). We introduce the idea of Multi-View Geo-Localisation (MVGL) - localising in two steps within a graph. First using CVGL to retrieve the most likely primary nodes (road junctions), before performing relative pose estimation also candidate edges to increase precision. The corresponding initial network proposed reduced median error from hundreds of metres down to approximately 22m error across the entire dataset.

Current autonomous driving algorithms heavily rely on the visible spectrum, which is prone to performance degradation in adverse conditions like fog, rain, snow, glare, and high contrast. Although other spectral bands like near-infrared (NIR) and long-wave infrared (LWIR) can enhance vision perception in such situations, they have limitations and lack large-scale datasets and benchmarks. Short-wave infrared (SWIR) imaging offers several advantages over NIR and LWIR. However, no publicly available large-scale datasets currently incorporate SWIR data for autonomous driving. To address this gap, we introduce the RGB and SWIR Multispectral Driving (RASMD) dataset, which comprises 100,000 synchronized and spatially aligned RGB-SWIR image pairs collected across diverse locations, lighting, and weather conditions. In addition, we provide a subset for RGB-SWIR translation and object detection annotations for a subset of challenging traffic scenarios to demonstrate the utility of SWIR imaging through experiments on both object detection and RGB-to-SWIR image translation. Our experiments show that combining RGB and SWIR data in an ensemble framework significantly improves detection accuracy compared to RGB-only approaches, particularly in conditions where visible-spectrum sensors struggle. We anticipate that the RASMD dataset will advance research in multispectral imaging for autonomous driving and robust perception systems.

UDA-CH contains 16 objects that cover a variety of artworks which can be found in a museum like sculptures, paintings and books. Specifically, the dataset has been collected inside the cultural site “Galleria Regionale di Palazzo Bellomo” located in Siracusa, Italy. Source: An Unsupervised Domain Adaptation Scheme for Single-Stage Artwork Recognition in Cultural Sites

75k photos of windows + 21k synthetic renders of building windows.