We propose a high-quality transparent visualization method suitable for large-scale laser-scanned point clouds. We call the method “stochastic point-based rendering (SPBR),” which is based on a novel stochastic algorithm. SPBR enables us to clearly observe the deep interior of laser-scanned 3D objects with the correct feeling of depth. The high quality of SPBR originates from the effect of “stochastic noise transparentization,” which is an effect to make the measurement noise transparent and invisible in the created images. We mathematically prove that this effect also makes the created transparent images coincide with the results of the conventional methods based on the alpha blending, which is time-consuming and impractical for large-scale laser-scanned point clouds. We also demonstrate the effectiveness of SPBR by applying it to modern buildings, cultural heritage objects, forests, and a factory. For all of the cases, the method works quite well, realizing clear and correct 3D see-through imaging of the laser-scanned objects.

我们提出一种适用于大规模激光扫描点云的高质量透明可视化方法。我们将这种方法称为“基于随机点的渲染（SPBR）”，该方法基于一种新颖的随机算法。SPBR使我们能够以正确的深度感觉清晰地观察激光扫描3D对象的深层内部。SPBR的高质量源于“随机噪声透明化”的效果，“随机噪声透明化”是一种使测量噪声在创建的图像中透明且不可见的效果。我们从数学上证明了这种效果还可以使创建的透明图像与基于alpha混合的常规方法的结果一致，这对于大型激光扫描点云而言既费时又不切实际。我们还将SPBR应用于现代建筑，文化遗产，森林和工厂，以证明SPBR的有效性。在所有情况下，该方法都能很好地工作，实现对激光扫描对象的清晰，正确的3D透视成像。