Biased Cars Dataset

We introduce a challenging, photo-realistic dataset for analyzing out-of-distribution performance in computer vision: the Biased-Cars dataset. Our dataset features outdoor scene data with fine control over scene clutter (trees, street furniture, and pedestrians), car colors, object occlusions, diverse backgrounds (building/road textures) and lighting conditions (sky maps). Biased-Cars consists of 30K images of five different car models with different car colors seen from different viewpoints car colors varying between 0-90 degrees of azimuth, and 0-50 degrees of zenith across multiple scales. We provide labels for car model, color, viewpoint and scale. We also provide semantic label maps for background categories including road, sky, pavement, pedestrians, trees and buildings. Our dataset offers complete control over the joint distribution of categories, viewpoints, and other scene parameters, and the use of physically based rendering ensures photo-realism.我们提出了一款用于分析计算机视觉领域分布外（out-of-distribution）性能的照片级真实感数据集：偏置汽车（Biased-Cars）数据集。该数据集包含室外场景数据，可对场景干扰元素（树木、街道设施与行人）、汽车颜色、物体遮挡情况、多样化背景（建筑/道路纹理）以及光照条件（天空贴图）进行精细调控。偏置汽车数据集共计3万张图像，涵盖5种不同车型，搭配多种汽车颜色，可从不同视角拍摄；视角方位角覆盖0°至90°、天顶角覆盖0°至50°，且包含多种尺度变化。我们为该数据集提供了车型、颜色、视角及尺度的标注信息，同时还提供了道路、天空、人行道、行人、树木与建筑等背景类别的语义标注图。本数据集可实现对类别、视角及其他场景参数联合分布的完全可控，且采用基于物理的渲染（physically based rendering）技术，确保了图像的照片级真实感。