点群のリアルタイムマルチモデルパラメトリック表現
A Real-Time Multi-Model Parametric Representation of Point Clouds
点群をガウス混合モデルでクラスタ分割し、平面やBスプライン曲面としてリアルタイムに検出・フィッティングする手法を提案。従来より高精度・高効率で、低電力オンボード計算機上で36.4fpsを実現した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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分類: cs.CV, cs.RO
原文アブストラクト
In recent years, parametric representations of point clouds have been widely applied in tasks such as memory-efficient mapping and multi-robot collaboration. Highly adaptive models, like spline surfaces or quadrics, are computationally expensive in detection or fitting. In contrast, real-time methods, such as Gaussian mixture models or planes, have low degrees of freedom, making high accuracy with few primitives difficult. To tackle this problem, a multi-model parametric representation with real-time surface detection and fitting is proposed. Specifically, the Gaussian mixture model is first employed to segment the point cloud into multiple clusters. Then, flat clusters are selected and merged into planes or curved surfaces. Planes can be easily fitted and delimited by a 2D voxel-based boundary description method. Surfaces with curvature are fitted by B-spline surfaces and the same boundary description method is employed. Through evaluations on multiple public datasets, the proposed surface detection exhibits greater robustness than the state-of-the-art approach, with 3.78 times improvement in efficiency. Meanwhile, this representation achieves a 2-fold gain in accuracy over Gaussian mixture models, operating at 36.4 fps on a low-power onboard computer.
