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首页> 外文期刊>ACM Transactions on Graphics >Noise-Resilient Reconstruction of Panoramas and 3D Scenes Using Robot-Mounted Unsynchronized Commodity RGB-D Cameras
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Noise-Resilient Reconstruction of Panoramas and 3D Scenes Using Robot-Mounted Unsynchronized Commodity RGB-D Cameras

机译：利用机器人安装的非同步商品RGB-D CamerAs的全景和3D场景的噪声弹性重建

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摘要

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.
机译：我们展示了一个两阶段方法来首先构建3D全景，然后针对大规模室内场景的噪声弹性重建来缝合它们。我们的方法需要多个未同步的RGB-D相机，安装在机器人平台上，这可以在场景中的不同位置执行就地旋转。这种相机在共同（但未知）轴上旋转，其提供了一种用于应对未同步的相机的新型视角，而不需要其视野（FOV）的足够重叠。基于该关键观察，我们提出了新的算法同时跟踪这些相机。此外，在将原始帧的集成到昼夜平行中，我们从分配给相同像素的多个测量结果导出不确定性估计。这使我们能够适当地模拟传感噪声并考虑其影响力，从而实现更好的噪声弹性，并提高每个全景的几何质量和全球全景的准确性。我们评估并展示我们提出的方法来提高现实世界和合成扫描的场景重建几何质量。

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