Scene reconstruction with consumer-level RGB-D cameras has developed considerable momentum in both robotics and vision communities. In the literature of robotics, high-quality camera tracking, the key to accurate reconstruction, is challenging in geometric featureless scenes or under large lighting variation. In this letter, we propose an accurate RGB-D reconstruction approach, by incorporating 3D line features into key steps of the pipeline to significantly improve tracking robustness and reduce error accumulation. Our reconstruction approach is characterized by three key designs. First, we propose a robust 3D line extractor to efficiently extract accurate and consistent 3D lines from RGB-D data. Moreover, we design a joint objective for frame-to-model scan alignment considering both dense points and 3D lines in a robust error metric. With extra constraints from 3D lines and the robust error metric, scan alignment is further enhanced in challenging situations even without any heuristics for spurious correspondence pruning. Finally, we propose an efficient submap-based hierarchical optimization considering both point and 3D line correspondences to significantly reduce the accumulated error in camera tracking and obtain an accurate global 3D model. Extensive experiments on synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art RGB-D reconstruction approaches for common indoor or outdoor scenes in challenging situations of practical scanning.

中文翻译：

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具有线特征约束的稳健且准确的 RGB-D 重建


使用消费级 RGB-D 相机进行场景重建在机器人和视觉领域已经发展了相当大的势头。在机器人技术文献中，高质量相机跟踪是准确重建的关键，但在无几何特征的场景或大光照变化下具有挑战性。在这封信中，我们提出了一种精确的 RGB-D 重建方法，通过将 3D 线特征合并到流程的关键步骤中，以显着提高跟踪鲁棒性并减少误差累积。我们的重建方法具有三个关键设计的特点。首先，我们提出了一个强大的 3D 线条提取器，可以有效地从 RGB-D 数据中提取准确且一致的 3D 线条。此外，我们设计了帧到模型扫描对齐的联合目标，同时考虑了稳健误差度量中的密集点和 3D 线。借助 3D 线的额外约束和稳健的误差度量，即使没有任何虚假对应修剪的启发式方法，扫描对齐在具有挑战性的情况下也能得到进一步增强。最后，我们提出了一种考虑点和 3D 线对应的有效的基于子图的分层优化，以显着减少相机跟踪中的累积误差并获得准确的全局 3D 模型。对合成数据集和真实数据集的大量实验表明，在具有挑战性的实际扫描情况下，对于常见的室内或室外场景，我们的方法优于最先进的 RGB-D 重建方法。