Real-time 3D Reconstruction at Scale using Voxel Hashing
Real-time 3D Reconstruction at Scale using Voxel Hashing
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DOI：
10.1145/2508363.2508374
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发表时间：
2013-11-01
影响因子：
6.2
通讯作者：
Stamminger, Marc
中科院分区：
文献类型：
--
作者：
Niessner, Matthias;Zollhoefer, Michael;Stamminger, Marc
由于实时消费者深度相机的出现，在线 3D 重建正在引起新的兴趣。基本问题将实时重叠深度图作为输入，并逐步将它们融合到单个 3D 模型中。这是一个挑战，特别是当需要实时性能但没有交易质量或规模时。我们贡献了一个基于内存和速度高效的数据结构的在线系统，用于大规模和精细的体积重建。我们的系统使用简单的空间哈希方案来压缩空间，并允许实时访问和更新隐式表面数据，而不需要常规或分层网格数据结构。表面数据仅在观察测量的地方密集存储。此外，数据可以有效地流入或流出哈希表，从而在传感器运动期间实现进一步的可扩展性。我们展示了各种场景的交互式重建，重建了细粒度的细节和大规模的环境。我们说明了如何在商用图形硬件上实时执行深度图预处理、相机姿态估计、深度图融合和表面渲染等管道的所有部分。最后，我们与当前最先进的在线系统进行了比较，展示了性能和重建质量的改进。
Online 3D reconstruction is gaining newfound interest due to the availability of real-time consumer depth cameras. The basic problem takes live overlapping depth maps as input and incrementally fuses these into a single 3D model. This is challenging particularly when real-time performance is desired without trading quality or scale. We contribute an online system for large and fine scale volumetric reconstruction based on a memory and speed efficient data structure. Our system uses a simple spatial hashing scheme that compresses space, and allows for real-time access and updates of implicit surface data, without the need for a regular or hierarchical grid data structure. Surface data is only stored densely where measurements are observed. Additionally, data can be streamed efficiently in or out of the hash table, allowing for further scalability during sensor motion. We show interactive reconstructions of a variety of scenes, reconstructing both fine-grained details and large scale environments. We illustrate how all parts of our pipeline from depth map pre-processing, camera pose estimation, depth map fusion, and surface rendering are performed at real-time rates on commodity graphics hardware. We conclude with a comparison to current state-of-the-art online systems, illustrating improved performance and reconstruction quality.