CADillac

CADillac dataset is a collection of over 1,000 high-quality 3D models of vehicles. The primary goal is to render these 3D models into photo-realistic images, that, in turn, can be used to train machine learning models for car detection. Each of the CAD model comes in .blend format (native format of Blender renderer.) CAD models are centered in the 3D scene and properly sized. They are complemented with meta-information about the car: the dimensions, color, type, domain, and more. Meta-information is stored in file "collection_v2.json". Please see README.txt for the detailed description of the dataset. The dataset is complemented with the code available at https://github.com/kukuruza/CADillac. This code allows to (1) view and change CAD models in the dataset and (2) render CAD models to create virtual images. Post further question in the Github repository. CAD models were originally collected from 3DWarehouse repository [1]. 3DWarehouse gave us the permission to change and distribute the models as a part of these dataset. One similar dataset we are aware of is Carla [6]. It contains a set of 3D models with a texture bank. The total number of differing combinations of cars and textures Carla is able to generate is of the order of thousand(s). CADillac adds the power of crowdsourcing -- our dataset includes rare cases, such as military and fictional cars, fancy car tuning, antique cars, a variety of trucks and emergency vehicles and more. -----------Methodology----------- We collected CAD vehicle models at 3DWarehouse [1] file sharing platform specialized at 3D CAD models. 3DWarehouse generously granted us the permission to use the data for the project. Models are contributed by individual artists and are accompanied by the name and the description. Fortunately, there are many detailed and high-quality models of vehicles, often organized in collections. We download individual collections automatically using the Selenium [2] web-browser automation tool. After the models were downloaded, we manually classified them by type (passenger, bus, truck, van, and bike), by domain (general, emergency, military, and fiction), and by color (white, black, gray, red, yellow, blue, green, and mixed). Additionally, we manually extracted the information about car make, model, and year from the original model name and description, when such information is available. Using this information, we further searched for physical dimensions of the models using the CarQueryApi database [3]. In particular, we retrieved vehicle length, width, height, and wheel base. We found the dimensions of the roughly 70% of the models in the dataset, while the missing 30% either do not have a proper description, or are missing from the CarQueryApi database. After that, CAD models were post-processed. Each model was oriented along the X- axis, centered, and scaled to match the information from CarQueryApi. We found that the wheel base is the most robust property to match, since length, width, and height of a CAD model may be affected by bumper bars, side mirrors, taxi checkers or emergency light bar, and so on. CAD models originally come in format skp, which is the native format of Sketchup [4] 3D modelling software. They are converted to Blender [5] native format blend. Due to partial incompatibility of the two formats, some models get triangular artifacts on some surfaces, while others get matte gray artificial looking glass surfaces. These appearance problems are recorded in "collection_v2.json". -----------Acknowledgements----------- The authors thank Spandan Gandhi and Ekaterina Toropova for their help in preparing the dataset.

CADillac 数据集是一个包含逾 1000 个高质量车辆三维模型的集合。其核心目标是将这些三维模型渲染为照片级真实感图像，进而用于训练车辆检测相关的机器学习模型。每个 CAD 模型均采用 .blend 格式（Blender 渲染器的原生格式）存储。所有 CAD 模型均已在三维场景中完成居中对齐与尺寸标准化，并附带车辆的元信息，包括尺寸、颜色、类型、应用领域等。元信息存储于 "collection_v2.json" 文件中。如需了解数据集的详细说明，请参阅 README.txt。本数据集配套有开源代码，托管于 https://github.com/kukuruza/CADillac，该代码可实现两项功能：(1) 浏览与修改数据集中的 CAD 模型；(2) 渲染 CAD 模型以生成虚拟图像。若有进一步疑问，可在该 GitHub 仓库中提出。

CAD 模型最初采集自 3DWarehouse 仓库[1]，该仓库已授权我们修改并分发这些模型作为本数据集的一部分。我们所知的类似数据集包括 Carla[6]，其包含一组带有纹理库的三维模型，可生成的车辆与纹理组合总数约为千量级。CADillac 数据集则借助众包的优势，涵盖了诸如军用车辆、虚构车辆、改装豪车、古董车、各类卡车及应急车辆等罕见车型。

-----------Methodology-----------
我们从专注于三维 CAD 模型的文件共享平台 3DWarehouse[1] 采集车辆 CAD 模型，该平台慷慨授权我们将数据用于本项目。这些模型由个人艺术家创作，附带名称与描述信息。值得庆幸的是，平台上存在大量精细且高质量的车辆模型，且常以集合形式组织。我们使用 Selenium[2] 网页自动化工具自动下载各个模型集合。

模型下载完成后，我们手动对其进行分类：按类型分为乘用车、巴士、卡车、厢式车与自行车；按应用领域分为通用、应急、军用与虚构类；按颜色分为白色、黑色、灰色、红色、黄色、蓝色、绿色与混合色。此外，若原始模型名称与描述中包含相关信息，我们会手动从中提取车辆品牌、型号及生产年份。

基于上述信息，我们通过 CarQueryApi 数据库[3] 进一步查询模型的物理尺寸，具体获取了车辆的长度、宽度、高度与轴距。我们成功为数据集中约 70% 的模型获取到了尺寸信息，剩余 30% 的模型要么缺乏足够的描述信息，要么未在 CarQueryApi 数据库中收录。

随后我们对 CAD 模型进行后处理：将每个模型沿 X 轴对齐、居中放置，并根据 CarQueryApi 获取的尺寸信息进行缩放。我们发现轴距是最可靠的匹配属性，因为 CAD 模型的长、宽、高可能会因保险杠、后视镜、出租车标识牌或应急灯条等细节而产生偏差。

原始 CAD 模型采用 skp 格式，即 Sketchup[4] 三维建模软件的原生格式，我们将其转换为 Blender[5] 原生的 blend 格式。由于两种格式存在部分兼容性问题，部分模型的表面会出现三角面伪影，另有部分模型的玻璃表面会呈现出哑光灰色的人工质感。这些外观问题均记录在 "collection_v2.json" 文件中。

-----------Acknowledgements-----------
作者感谢 Spandan Gandhi 与 Ekaterina Toropova 在数据集筹备过程中提供的帮助。