Scharr Hanno, Briese Christoph, Embgenbroich Patrick, Fischbach Andreas, Fiorani Fabio, Müller-Linow Mark
Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich, Germany.
Front Plant Sci. 2017 Sep 28;8:1680. doi: 10.3389/fpls.2017.01680. eCollection 2017.
Volume carving is a well established method for visual hull reconstruction and has been successfully applied in plant phenotyping, especially for 3d reconstruction of small plants and seeds. When imaging larger plants at still relatively high spatial resolution (≤1 mm), well known implementations become slow or have prohibitively large memory needs. Here we present and evaluate a computationally efficient algorithm for volume carving, allowing e.g., 3D reconstruction of plant shoots. It combines a well-known multi-grid representation called "Octree" with an efficient image region integration scheme called "Integral image." Speedup with respect to less efficient octree implementations is about 2 orders of magnitude, due to the introduced refinement strategy "Mark and refine." Speedup is about a factor 1.6 compared to a highly optimized GPU implementation using equidistant voxel grids, even without using any parallelization. We demonstrate the application of this method for trait derivation of banana and maize plants.
体素雕刻是一种成熟的视觉外壳重建方法,已成功应用于植物表型分析,特别是用于小植株和种子的三维重建。当以相对较高的空间分辨率(≤1毫米)对较大植株进行成像时,已知的实现方法会变得很慢,或者内存需求大得令人望而却步。在此,我们提出并评估一种计算效率高的体素雕刻算法,例如可用于植物枝条的三维重建。它将一种名为“八叉树”的知名多网格表示法与一种名为“积分图像”的高效图像区域积分方案相结合。由于引入了“标记与细化”的细化策略,相对于效率较低的八叉树实现方法,速度提高了约2个数量级。即使不使用任何并行化,与使用等距体素网格的高度优化的GPU实现方法相比,速度也提高了约1.6倍。我们展示了该方法在香蕉和玉米植株性状推导中的应用。