TurboReg: TurboClique for Robust and Efficient Point Cloud Registration

TL;DR

TurboReg introduces a fast, robust point cloud registration method using a novel lightweight clique and a highly parallelizable search algorithm, significantly improving speed and accuracy over existing methods.

Contribution

It proposes TurboClique and PGS algorithms for efficient, robust point cloud registration, reducing complexity from exponential to linear and enhancing practical performance.

Findings

TurboReg is 208 times faster than 3DMAC on a benchmark dataset.

Achieves higher recall and robustness in real-world datasets.

Operates efficiently with linear time complexity.

Abstract

Robust estimation is essential in correspondence-based Point Cloud Registration (PCR). Existing methods using maximal clique search in compatibility graphs achieve high recall but suffer from exponential time complexity, limiting their use in time-sensitive applications. To address this challenge, we propose a fast and robust estimator, TurboReg, built upon a novel lightweight clique, TurboClique, and a highly parallelizable Pivot-Guided Search (PGS) algorithm. First, we define the TurboClique as a 3-clique within a highly-constrained compatibility graph. The lightweight nature of the 3-clique allows for efficient parallel searching, and the highly-constrained compatibility graph ensures robust spatial consistency for stable transformation estimation. Next, PGS selects matching pairs with high SC$^2$ scores as pivots, effectively guiding the search toward TurboCliques with higher inlier ratios. Moreover, the PGS algorithm has linear time complexity and is significantly more efficient than the maximal clique search with exponential time complexity. Extensive experiments show that TurboReg achieves state-of-the-art performance across multiple real-world datasets, with substantial speed improvements. For example, on the 3DMatch+FCGF dataset, TurboReg (1K) operates $208.22\times$ faster than 3DMAC while also achieving higher recall. Our code is accessible at \href{https://github.com/Laka-3DV/TurboReg}{\texttt{TurboReg}}.