SVIRO：合成车辆内部后排座椅占用数据集和基准
SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark
摘要：
我们发布了SVIRO，这是一个针对10种不同车辆乘客舱中场景的合成数据集，以便分析基于机器学习的方法在有限数量的变化（例如，相同的背景和纹理，每个类的实例很少）上训练时的泛化能力和可靠性。这与普通基准数据集固有的高可变性形成对比，后者专注于改进一般任务的最新技术水平。我们的数据集包含用于目标检测的边界框、实例分割掩模、用于姿态估计的关键点和每个合成场景的深度图像以及用于分类的每个单独座位的图像。我们的用例的优势是双重的：接近现实的应用程序，在新的情况下对新方法进行基准测试，同时将复杂性降低到更易于处理的环境中，这样应用程序和理论问题就可以在更具挑战性的数据集上作为玩具问题进行测试。数据和评估服务器可在https://sviro.kl.dfki.de上获得。
英文摘要：
We release SVIRO, a synthetic dataset for sceneries in the passenger compartment of ten different vehicles, in order to analyze machine learning-based approaches for their generalization capacities and reliability when trained on a limited number of variations (e.g. identical backgrounds and textures, few instances per class). This is in contrast to the intrinsically high variability of common benchmark datasets, which focus on improving the state-of-the-art of general tasks. Our dataset contains bounding boxes for object detection, instance segmentation masks, keypoints for pose estimation and depth images for each synthetic scenery as well as images for each individual seat for classification. The advantage of our use-case is twofold: The proximity to a realistic application to benchmark new approaches under novel circumstances while reducing the complexity to a more tractable environment, such that applications and theoretical questions can be tested on a more challenging dataset as toy problems. The data and evaluation server are available under https://sviro.kl.dfki.de.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
