通过双空间过滤与强化，实现快速高精度刚性配准
DualReg: Dual-Space Filtering and Reinforcement for Rigid Registration
- 结合特征匹配与局部几何匹配优势，分两步优化配准过程
- 在KITTI数据集上比MAC快32倍，精度相当
- 适合需要实时处理的点云配准场景
噪声数据、部分重叠以及实时处理需求给刚性配准带来重大挑战。针对基于特征的匹配虽能处理大变换但精度有限，而基于局部几何的匹配虽可实现精细对齐但依赖良好初始变换的问题，我们提出一种新颖的双空间范式，充分融合两种方法的优势。首先，设计一种高效过滤机制，包含轻量级单点RANSAC算法和后续精化模块，剔除不可靠的特征对应点；随后，将筛选后的对应点作为锚点，提取几何代理，构建定制化目标函数并使用专用求解器估计变换。实验验证了该方法的有效性：在KITTI数据集上相比MAC实现32倍的CPU时间加速，且精度相当。项目页面：https://ustc3dv.github.io/DualReg/
原文摘要 · Abstract (English)
Noisy, partially overlapping data and the need for real-time processing pose major challenges for rigid registration. Considering that feature-based matching can handle large transformation differences but suffers from limited accuracy, while local geometry-based matching can achieve fine-grained local alignment but relies heavily on a good initial transformation, we propose a novel dual-space paradigm to fully leverage the strengths of both approaches. First, we introduce an efficient filtering mechanism consisting of a computationally lightweight one-point RANSAC algorithm and a subsequent refinement module to eliminate unreliable feature-based correspondences. Subsequently, we treat the filtered correspondences as anchor points, extract geometric proxies, and formulate an effective objective function with a tailored solver to estimate the transformation. Experiments verify our method's effectiveness, as demonstrated by a 32x CPU-time speedup over MAC on KITTI with comparable accuracy. Project page: https://ustc3dv.github.io/DualReg/.
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