Virtualized Point Cloud Rendering.

Jose A Collado, Alfonso Lopez, Juan M Jurado, J Roberto Jimenez
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Abstract

Remote sensing technologies, such as LiDAR, produce billions of points that commonly exceed the storage capacity of the GPU, restricting their processing and rendering. Level of detail (LoD) techniques have been widely investigated, but building the LoD structures is also time-consuming. This study proposes a GPU-driven culling system focused on determining the number of points visible in every frame. It can manipulate point clouds of any arbitrary size while maintaining a low memory footprint in both the CPU and GPU. Instead of organizing point clouds into hierarchical data structures, these are split into groups of points sorted using the Hilbert encoding. This alternative alleviates the occurrence of anomalous groups found in Morton curves. Instead of keeping the entire point cloud in the GPU, points are transferred on demand to ensure real-time capability. Accordingly, our solution can manipulate huge point clouds even in commodity hardware with low memory capacities. Moreover, hole filling is implemented to cover the gaps derived from insufficient density and our LoD system. Our proposal was evaluated with point clouds of up to 18 billion points, achieving an average of 80 frames per second (FPS) without perceptible quality loss. Relaxing memory constraints further enhances visual quality while maintaining an interactive frame rate. We assessed our method on real-world data, comparing it against three state-ofthe- art methods, demonstrating its ability to handle significantly larger point clouds. The code is available on https://github.com/Krixtalx/Nimbus.

虚拟化点云渲染。
遥感技术,如激光雷达,产生数十亿个点,通常超过GPU的存储容量,限制了它们的处理和渲染。详细层次(LoD)技术已被广泛研究,但构建LoD结构也很耗时。本研究提出一种gpu驱动的剔除系统,专注于确定每帧中可见点的数量。它可以操作任意大小的点云,同时在CPU和GPU中保持较低的内存占用。与将点云组织成分层数据结构不同,这些点云被分成使用希尔伯特编码排序的点组。这种选择减轻了Morton曲线中发现的异常群的发生。而不是将整个点云保持在GPU中,点是按需传输的,以确保实时能力。因此,我们的解决方案甚至可以在低内存容量的商用硬件中操作巨大的点云。此外,为了弥补由于密度不足和我们的LoD系统而产生的空隙,还实施了填孔。我们的建议在高达180亿个点云的情况下进行了评估,平均每秒80帧(FPS),没有明显的质量损失。放松内存限制进一步提高视觉质量,同时保持交互帧率。我们在真实世界的数据上评估了我们的方法,将其与三种最先进的方法进行比较,证明了它处理更大点云的能力。代码可在https://github.com/Krixtalx/Nimbus上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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