Abstract
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.
Abstract (translated)
近年来，点云的参数表示在诸如记忆高效地图构建和多机器人协作等任务中得到了广泛应用。高度自适应模型（如样条曲面或二次曲面）在检测或拟合过程中计算成本较高。相比之下，实时方法（例如高斯混合模型或平面模型），虽然自由度较低，但有助于提高效率，不过用少数几个基本元素实现高精度较为困难。为解决这一问题，提出了一种结合了多种模型的参数表示法，实现了实时表面检测和拟合。 具体来说，首先采用高斯混合模型将点云分割成多个簇。然后选择平面簇并将其合并成平面或曲面。平面可以通过基于二维体素边界描述的方法轻松拟合和限定。对于带有曲率的表面，则使用B样条曲面进行拟合，并且应用相同边界的描述方法。 通过在多个公开数据集上的评估，所提出的表面检测方法比现有最佳方法显示出更强的鲁棒性，在效率方面提高了3.78倍。同时，该表示法在准确性上较高斯混合模型提升了两倍，在低功耗车载计算机上的运行速度达到每秒36.4帧。
URL
https://arxiv.org/abs/2509.14773
