Road Scene Dataset. We open-source our code and dataset and Kaggle is the world’s larg
We open-source our code and dataset and Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. [Unlabeled Image Pairs] RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving Unlock detailed insights into road scenes with our Vehicle Image Captioning Dataset. Each group We introduce the largest dataset of rainy street scene images to date, to support research in self-driving. Featuring over 1000 high-resolution images We perform extensive transfer learning experiments and ablation studies on the RoadSense3D dataset, the TUM Traffic datasets, and the DAIR-V2X dataset. Dataset of Indian Highways: Ideal for Autonomous Vehicle & Lane Departure System IDD3D: Indian Driving Dataset for 3D Unstructured Roads, Published at WACV 2023. Existing fusion methods are typically helpless in dealing with degradations in Road scene understanding is crucial in autonomous driving, enabling machines to perceive the visual environment. We define 13 major classes for annotation: road, sidewalk, building, traffic light, traffic sign, vegetation, sky, person, rider, car, bus, motorcycle, and bicycle, as Image fusion aims to combine information from different source images to create a comprehensively representative image. The images cover a wide range of realistic rain-induced ar-tifacts, including fog, droplets, and road In this paper, we present RSUD20K, a new dataset for road scene understanding, comprised of over 20K high-resolution images from the driving perspective on Bangladesh roads, . The Cityscapes Dataset We present a new large-scale dataset that contains a diverse set of stereo video sequences recorded in street scenes from 50 In the case of automotive datasets, a scene semantic can be used for scene classification, such as buildings, sidewalks, parking lots, and other construction that could represent Dataset for Highway Traffic Analysis through CCTV captured footage. Each group includes both real-world and synthetic datasets, designed to support research in autonomous driving and computer vision. These images are highly representative The Road Scene Dataset is a curated collection of high-quality This challenging dataset encompasses diverse road scenes, narrow streets and highways, featuring objects from different viewpoints and scenes from crowded environments with By leveraging Lumion, this dataset aims to provide high-quality synthetic traffic scenes that can complement real-world data. However, recent object detectors tailored for learning on datasets This dataset specializes in panoptic segmentation, annotating every identifiable instance within the images, such as vehicles, roads, lane Our dataset, KITTI-Materials, is based on the well-established KITTI dataset and consists of 1000 frames covering 24 different road scenes of urban/suburban landscapes, carefully annotated This dataset provides a comprehensive collection of traffic scene datasets, categorized into three main groups: Traffic Scene Datasets, Top-View Datasets, and Depth Datasets. The dataset provides detailed annotations for 3D semantic occupancy prediction and road surface elevation reconstruction, offering a comprehensive representation of unstructured scenes. Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles Worldwide Road Scene Semantic Segmentation Dataset Click the markers in the above map to see dataset examples of the seleted city. This datset has 221 aligned Vis and IR image pairs containing rich scenes such as roads, vehicles, pedestrians and so on. Existing fusion methods are typically helpless in dealing with degradations in Our new dataset, KITTI-Materials, based on the well-established KITTI dataset, consists of 1000 frames covering 24 different road scenes of urban/suburban The dataset provides detailed annotations for 3D semantic occupancy prediction and road surface elevation reconstruction, offering a comprehensive representation of unstructured scenes. Image fusion aims to combine information from different source images to create a comprehensively representative image. We introduce RoScenes, the largest multi-view roadside perception dataset, which aims to shed light on the development of vision-centric Bird's Eye View (BEV) approaches for more challenging traffic scenes.
