PrimiTect: プリミティブ検出のための高速連続ハフ投票
PrimiTect: Fast Continuous Hough Voting for Primitive Detection
3D点群を平面や円錐などの幾何プリミティブに分類し、初期化不要で高精度・高効率に形状と姿勢を推定するハフ投票手法を提案した論文。
著者: Christiane Sommer, Yumin Sun, Erik Bylow, Daniel Cremers
分類: cs.CV, cs.RO
原文アブストラクト
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.
