State of the Art on 3D Reconstruction with RGB-D Cameras
State of the Art on 3D Reconstruction with RGB-D Cameras
复制标题
DOI：
10.1111/cgf.13386
复制
发表时间：
2018-05-01
影响因子：
2.5
通讯作者：
Kolb, Andreas
中科院分区：
文献类型：
--
作者：
Zollhoefer, Michael;Stotko, Patrick;Kolb, Andreas
经济实惠的消费级RGB-D相机的出现带来了视觉场景重建方法的深刻进步。计算机图形学和计算机视觉研究人员都花费了大量精力来开发全新的算法，以便使用RGB-D相机捕获静态和动态场景的全面形状模型。这在沿着几个维度上导致了现有技术的显著进步。一些方法实现非常高的重建细节，尽管有限的传感器分辨率。其他甚至实现实时性能，但可能质量较低。开发了新的概念，以在更大的空间和时间范围内捕获场景。其他最近的算法侧翼形状重建与并发的材料和照明估计，即使在一般的场景和无约束条件。在这份最新的报告中，我们详细分析了RGB-D场景重建的最新发展，并回顾了必要的相关工作。我们解释、比较和批判性分析了实现这些最新进步的常见底层算法概念。此外，我们展示了如何设计算法，以最好地利用RGB-D数据的好处，同时抑制其往往非平凡的数据失真。此外，本报告还确定和讨论了重要的开放研究问题，并为今后的工作提出了相关方向。
The advent of affordable consumer grade RGB-D cameras has brought about a profound advancement of visual scene reconstruction methods. Both computer graphics and computer vision researchers spend significant effort to develop entirely new algorithms to capture comprehensive shape models of static and dynamic scenes with RGB-D cameras. This led to significant advances of the state of the art along several dimensions. Some methods achieve very high reconstruction detail, despite limited sensor resolution. Others even achieve real-time performance, yet possibly at lower quality. New concepts were developed to capture scenes at larger spatial and temporal extent. Other recent algorithms flank shape reconstruction with concurrent material and lighting estimation, even in general scenes and unconstrained conditions. In this state-of-the-art report, we analyze these recent developments in RGB-D scene reconstruction in detail and review essential related work. We explain, compare, and critically analyze the common underlying algorithmic concepts that enabled these recent advancements. Furthermore, we show how algorithms are designed to best exploit the benefits of RGB-D data while suppressing their often non-trivial data distortions. In addition, this report identifies and discusses important open research questions and suggests relevant directions for future work.