We present a two-stage approach to first constructing 3D panoramas and then stitching them for noise-resilient reconstruction of large-scale indoor scenes. Our approach requires multiple unsynchronized RGB-D cameras, mounted on a robot platform, which can perform in-place rotations at different locations in a scene. Such cameras rotate on a common (but unknown) axis, which provides a novel perspective for coping with unsynchronized cameras, without requiring sufficient overlap of their Field-of-View (FoV). Based on this key observation, we propose novel algorithms to track these cameras simultaneously. Furthermore, during the integration of raw frames onto an equirectangular panorama, we derive uncertainty estimates from multiple measurements assigned to the same pixels. This enables us to appropriately model the sensing noise and consider its influence, so as to achieve better noise resilience, and improve the geometric quality of each panorama and the accuracy of global inter-panorama registration. We evaluate and demonstrate the performance of our proposed method for enhancing the geometric quality of scene reconstruction from both real-world and synthetic scans.

中文翻译：

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使用安装在机器人上的非同步商品 RGB-D 相机对全景图和 3D 场景进行抗噪重建

我们提出了一种两阶段的方法，首先构建 3D 全景图，然后将它们拼接以对大规模室内场景进行抗噪重建。我们的方法需要多个不同步的 RGB-D 相机，安装在机器人平台上，可以在场景中的不同位置执行就地旋转。这种相机在一个共同（但未知）的轴上旋转，这为处理不同步的相机提供了一个新的视角，而不需要它们的视野 (FoV) 有足够的重叠。基于这一关键观察，我们提出了新的算法来同时跟踪这些相机。此外，在将原始帧集成到 equirectangular 全景图的过程中，我们从分配给相同像素的多个测量值中得出不确定性估计。这使我们能够对感知噪声进行适当的建模并考虑其影响，从而实现更好的噪声弹性，提高每个全景图的几何质量和全局全景间配准的准确性。我们评估并展示了我们提出的方法的性能，用于提高真实世界和合成扫描的场景重建的几何质量。