Abstract
Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise from the huge point number, complex distribution, and outliers of outdoor LiDAR scans. In addition, most existing registration works generally adopt a two-stage paradigm: They first find correspondences by extracting discriminative local features, and then leverage estimators (eg. RANSAC) to filter outliers, which are highly dependent on well-designed descriptors and post-processing choices. To address these problems, we propose an end-to-end transformer network (RegFormer) for large-scale point cloud alignment without any further post-processing. Specifically, a projection-aware hierarchical transformer is proposed to capture long-range dependencies and filter outliers by extracting point features globally. Our transformer has linear complexity, which guarantees high efficiency even for large-scale scenes. Furthermore, to effectively reduce mismatches, a bijective association transformer is designed for regressing the initial transformation. Extensive experiments on KITTI and NuScenes datasets demonstrate that our RegFormer achieves state-of-the-art performance in terms of both accuracy and efficiency.
Abstract (translated)
虽然点云注册在对象级别和室内场景方面取得了显著的进展,但大规模注册方法却很少有人探索。挑战主要来自户外激光雷达扫描点云的巨大点数量和复杂的分布以及其中的异常点。此外,大多数现有的注册工作通常采用两个阶段的模式:首先通过提取专属的局部特征找到匹配点,然后利用Estimator(例如RANSAC)过滤异常点,这些异常点 highly reliant on 设计良好的特征描述器和后续处理选择。为了解决这些问题,我们提出了一种端到端Transformer网络(RegFormer),可以在不需要进一步后处理的情况下大规模点云对齐。具体来说,我们提出了一种投影aware的分层Transformer,以捕获远程依赖并全局提取点特征以过滤异常点。我们的Transformer具有线性复杂性,即使对于大规模场景也保证高效率。此外,为了有效地减少不匹配,我们设计了一对角关联Transformer,以回归初始变换。对KITTI和NuScenes数据集的广泛实验表明,我们的RegFormer在精度和效率方面都实现了最先进的表现。
URL
https://arxiv.org/abs/2303.12384
