实时融合多种模型的点云参数化表示，兼顾精度与速度。
A Real-Time Multi-Model Parametric Representation of Point Clouds
- 用高斯混合模型分组点云，再合并为平面或曲面
- 相比高斯混合模型，精度提升一倍，运行速度达36.4帧/秒
- 适合低功耗设备上的实时点云建模与多机器人协作
近年来，点云的参数化表示广泛应用于内存高效地图构建和多机器人协作任务中。自适应性强的模型（如样条曲面或二次曲面）在检测或拟合时计算成本高；而实时方法（如高斯混合模型或平面）自由度低，难以用少量基元实现高精度。为此，本文提出一种支持实时表面检测与拟合的多模型参数化表示方法。首先使用高斯混合模型将点云分割为多个簇，随后将平面簇合并为平面或曲面，并采用基于2D体素的边界描述法进行拟合与界定。曲面则通过B样条曲面拟合并应用相同的边界描述法。在多个公开数据集上的评估表明，该方法比现有最优方法效率提升3.78倍，精度较高斯混合模型提升2倍，在低功耗机载计算机上实现36.4帧/秒的实时性能。
原文摘要 · Abstract (English)
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.
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