We present a novel framework for rigid point cloud registration. Our approach is based on the principles of mechanics and thermodynamics. We solve the registration problem by assuming point clouds as rigid bodies consisting of particles. Forces can be applied between both particle systems so that they attract or repel each other. These forces are used to cause rigid-body motion of one particle system toward the other, until both are aligned. The framework supports physics-based registration processes with arbitrary driving forces, depending on the desired behaviour. Additionally, the approach handles feature-enhanced point clouds, e.g., by colours or intensity values. Our framework is freely accessible for download. In contrast to already existing algorithms, our contribution is to precisely register high-resolution point clouds with nearly constant computational effort and without the need for pre-processing, sub-sampling or pre-alignment. At the same time, the quality is up to 28 percent higher than for state-of-the-art algorithms and up to 49 percent higher when considering feature-enhanced point clouds. Even in the presence of noise, our registration approach is one of the most robust, on par with state-of-the-art implementations.

中文翻译：

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高分辨率特征增强点云的有效注册

我们提出了一种用于刚性点云注册的新颖框架。我们的方法基于力学和热力学原理。我们通过将点云假定为由粒子组成的刚体来解决配准问题。可以在两个粒子系统之间施加力，以使它们彼此吸引或排斥。这些力用于引起一个粒子系统向另一个粒子系统的刚体运动，直到两个粒子系统对齐为止。该框架根据所需的行为支持具有任意驱动力的基于物理的注册过程。另外，该方法例如通过颜色或强度值来处理特征增强的点云。我们的框架可免费下载。与现有算法相比，我们的贡献在于，以几乎恒定的计算量来精确注册高分辨率点云，而无需进行预处理，子采样或预对准。同时，其质量比最新算法高出28％，而考虑特征增强点云时，其质量则高出49％。即使存在噪声，我们的注册方法也是最强大的方法之一，可与最新的实现方式相提并论。