鱼眼摄像机通常用于获得大视场监视,增强现实以及特别是汽车应用。尽管普遍存在,但很少有公共数据集用于详细评估鱼眼图像上的计算机视觉算法。我们发布了第一个广泛的鱼眼汽车数据集WoodScape,它以1906年发明了鱼眼摄像机的罗伯特·伍德的名字命名.WoodScape包括四个环视摄像机和一些任务,包括分割,深度估计,3D边界框检测和污染检测。实例级别的40个类的语义标注为10,000多个图像提供,并为超过100,000个图像提供其他任务的注释。我们希望鼓励社区适应鱼眼摄像机的计算机视觉模型,而不是天真的整改。
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This work provides an architecture to enable robotic grasp planning via shapecompletion. Shape completion is accomplished through the use of a 3Dconvolutional neural network (CNN). The network is trained on our own new opensource dataset of over 440,000 3D exemplars captured from varying viewpoints.At runtime, a 2.5D pointcloud captured from a single point of view is fed intothe CNN, which fills in the occluded regions of the scene, allowing grasps tobe planned and executed on the completed object. Runtime shape completion isvery rapid because most of the computational costs of shape completion areborne during offline training. We explore how the quality of completions varybased on several factors. These include whether or not the object beingcompleted existed in the training data and how many object models were used totrain the network. We also look at the ability of the network to generalize tonovel objects allowing the system to complete previously unseen objects atruntime. Finally, experimentation is done both in simulation and on actualrobotic hardware to explore the relationship between completion quality and theutility of the completed mesh model for grasping.
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