Abstract
The rapid development of 3D acquisition technology has made it possible to obtain point clouds of real-world terrains. However, due to limitations in sensor acquisition technology or specific requirements, point clouds often contain defects such as holes with missing data. Inpainting algorithms are widely used to patch these holes. However, existing traditional inpainting algorithms rely on precise hole boundaries, which limits their ability to handle cases where the boundaries are not well-defined. On the other hand, learning-based completion methods often prioritize reconstructing the entire point cloud instead of solely focusing on hole filling. Based on the fact that real-world terrain exhibits both global smoothness and rich local detail, we propose a novel representation for terrain point clouds. This representation can help to repair the holes without clear boundaries. Specifically, it decomposes terrains into low-frequency and high-frequency components, which are represented by B-spline surfaces and relative height maps respectively. In this way, the terrain point cloud inpainting problem is transformed into a B-spline surface fitting and 2D image inpainting problem. By solving the two problems, the highly complex and irregular holes on the terrain point clouds can be well-filled, which not only satisfies the global terrain undulation but also exhibits rich geometric details. The experimental results also demonstrate the effectiveness of our method.
Abstract (translated)
3D 采集技术的快速发展使得获取真实地形的三点云成为可能。然而，由于传感器采集技术的限制或具体要求，点云通常包含一些缺陷，如缺失数据导致的洞。为了修复这些洞，修复算法（inpainting algorithms）得到了广泛应用。然而，现有的传统修复算法依赖于精确的洞边界，这限制了它们在边界定义不明确的情况下的处理能力。另一方面，基于学习的修复方法通常优先重构整个点云，而不是仅仅关注洞填充。基于真实地形既表现出全局平滑性又富有局部细节的事实，我们提出了一个新颖的地形点云表示。这种表示可以帮助修复洞，而不仅仅是填充洞。具体来说，它将地形分解为低频和高频组件，分别用B-spline表面和相对高度图表示。这样，地形点云修复问题转化为B-spline表面拟合和2D图像修复问题。通过解决这两个问题，可以填充地形点云中的复杂且不规则的洞，不仅满足全局地形起伏，还展示了丰富的几何细节。实验结果也证明了我们的方法的有效性。
URL
https://arxiv.org/abs/2404.03572
