This article presents the first surfel-based method for multi-view 3D reconstruction of the boundary between known and unknown space. The proposed approach integrates multiple views from a moving depth camera and it generates a set of surfels that encloses observed empty space, i.e., it models both the boundary between empty and occupied space, and the boundary between empty and unknown space. One novelty of the method is that it does not require a persistent voxel map of the environment to distinguish between unknown and empty space. The problem is solved thanks to an incremental algorithm that computes the Boolean union of two surfel bounded volumes: the known volume from previous frames and the space observed from the current depth image. A number of strategies were developed to cope with errors in surfel position and orientation. The method, implemented on CPU and GPU, was evaluated on real data acquired in indoor scenarios, and it was compared against state of the art approaches. Results show that the proposed method has a low number of false positive and false negatives, it is faster than a standard volumetric algorithm, it has a lower memory consumption, and it scales better in large environments.

中文翻译：

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基于Surfel的已知空间和未知空间之间边界的增量重构。

本文介绍了第一种基于冲浪的方法，用于对已知空间和未知空间之间的边界进行多视图3D重建。所提出的方法集成了来自移动深度相机的多个视图，并生成了一组包围观察到的空白空间的surfel，即，它既模拟了空白空间又占据了空白空间，同时对空白空间和未知空间之间的边界进行了建模。该方法的新颖之处在于，它不需要环境的持久体素图即可区分未知空间和空白空间。借助增量算法，该问题得以解决，该算法可计算两个surfel有界体积的布尔并集：前一帧的已知体积和从当前深度图像观察到的空间。开发了许多策略来应对冲浪位置和方向的错误。方法，对在CPU和GPU上实现的数据进行了评估，并根据室内场景中获取的真实数据进行了评估，并将其与最新方法进行了比较。结果表明，所提出的方法误报率和误报率较低，比标准的体积算法更快，内存消耗更低，并且在大型环境中可扩展性更好。