Fast Gravitational Approach for Rigid Point Set Registration with Ordinary Differential Equations

TL;DR

The paper presents a physics-inspired, efficient gravitational method for rigid point set registration that handles large, noisy, and partial data with improved speed and accuracy over existing techniques.

Contribution

Introduces the Fast Gravitational Approach (FGA), a novel, scalable, physics-based algorithm for point set alignment utilizing ordinary differential equations and Barnes-Hut acceleration.

Findings

Achieves quasilinear computational complexity.

Handles partial overlaps and inhomogeneous densities effectively.

Outperforms existing methods in speed and accuracy on LiDAR data.

Abstract

This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA). In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field. The optimal alignment is obtained by explicit modeling of forces acting on the particles as well as their velocities and displacements with second-order ordinary differential equations of motion. Additional alignment cues (point-based or geometric features, and other boundary conditions) can be integrated into FGA through particle masses. We propose a smooth-particle mass function for point mass initialization, which improves robustness to noise and structural discontinuities. To avoid prohibitive quadratic complexity of all-to-all point interactions, we adapt a Barnes-Hut tree for accelerated force computation and achieve quasilinear computational complexity. We show that the new method class has characteristics not found in previous alignment methods such as efficient handling of partial overlaps, inhomogeneous point sampling densities, and coping with large point clouds with reduced runtime compared to the state of the art. Experiments show that our method performs on par with or outperforms all compared competing non-deep-learning-based and general-purpose techniques (which do not assume the availability of training data and a scene prior) in resolving transformations for LiDAR data and gains state-of-the-art accuracy and speed when coping with different types of data disturbances.