论文标题
原始：快速连续的霍夫投票对原始检测
PrimiTect: Fast Continuous Hough Voting for Primitive Detection
论文作者
论文摘要
本文在3D点集的上下文中解决了数据抽象问题。我们的方法将指点分类为不同的几何原语，例如平面和锥体，从而导致数据的紧凑表示。该方法基于半全球霍夫投票方案，不需要初始化，并且是稳健，准确和有效的。我们使用原语的局部，低维参数化来确定点所属对象的类型，形状和姿势。这使我们的算法适合在机器人应用程序中经常需要的计算能力低的设备上运行。评估表明，我们的方法在准确性和鲁棒性方面都优于最先进的方法。
This paper tackles the problem of data abstraction in the context of 3D point sets. Our method classifies points into different geometric primitives, such as planes and cones, leading to a compact representation of the data. Being based on a semi-global Hough voting scheme, the method does not need initialization and is robust, accurate, and efficient. We use a local, low-dimensional parameterization of primitives to determine type, shape and pose of the object that a point belongs to. This makes our algorithm suitable to run on devices with low computational power, as often required in robotics applications. The evaluation shows that our method outperforms state-of-the-art methods both in terms of accuracy and robustness.
