论文标题
phocal：具有光学挑战对象的类别级对象姿势估计的多模式数据集
PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects
论文作者
论文摘要
物体姿势估计对于机器人应用和增强现实至关重要。除了实例级别6D对象姿势估计方法之外，估计类别级别的姿势和形状已成为有希望的趋势。因此，需要良好设计的数据集需要一个新的研究领域。为了向社区提供具有高质量的地面真相注释的基准，我们为类别级的对象姿势估算介绍了一个多模式数据集，具有光学挑战性的对象，称为phocal。 Phocal包括60种高质量的家庭对象3D模型，包括高反射性，透明和对称对象。我们开发了一种新型的机器人支持的多模式（RGB，深度，极化）数据采集和注释过程。它可确保姿势的亚毫米准确性，用于不透明的纹理，闪亮和透明的物体，没有运动模糊和完美的相机同步。为了为我们的数据集设定基准，在富有挑战性的肺海场景中评估了最先进的RGB-D和单眼RGB方法。
Object pose estimation is crucial for robotic applications and augmented reality. Beyond instance level 6D object pose estimation methods, estimating category-level pose and shape has become a promising trend. As such, a new research field needs to be supported by well-designed datasets. To provide a benchmark with high-quality ground truth annotations to the community, we introduce a multimodal dataset for category-level object pose estimation with photometrically challenging objects termed PhoCaL. PhoCaL comprises 60 high quality 3D models of household objects over 8 categories including highly reflective, transparent and symmetric objects. We developed a novel robot-supported multi-modal (RGB, depth, polarisation) data acquisition and annotation process. It ensures sub-millimeter accuracy of the pose for opaque textured, shiny and transparent objects, no motion blur and perfect camera synchronisation. To set a benchmark for our dataset, state-of-the-art RGB-D and monocular RGB methods are evaluated on the challenging scenes of PhoCaL.
