About: We introduce a GPU-accelerated LOD construction process that creates a hybrid voxel-point-based variation of the widely used layered point cloud (LPC) structure for LOD rendering and streaming. The massive performance improvements provided by the GPU allow us to improve the quality of lower LODs via color filtering while still increasing construction speed compared to the non-filtered, CPU-based state of the art. Background: LOD structures are required to render hundreds of millions to trillions of points, but constructing them takes time. Results: LOD structures suitable for rendering and streaming are constructed at rates of about 1 billion points per second (with color filtering) to 4 billion points per second (sample-picking/random sampling, state of the art) on an RTX 3090 -- an improvement of a factor of 80 to 400 times over the CPU-based state of the art (12 million points per second). Due to being in-core, model sizes are limited to about 500 million points per 24GB memory. Discussion: Our method currently focuses on maximizing in-core construction speed on the GPU. Issues such as out-of-core construction of arbitrarily large data sets are not addressed, but we expect it to be suitable as a component of bottom-up out-of-core LOD construction schemes.
翻译:大约 : 我们引入了一个 GPU 加速的 LOD 建设过程, 以混合维xel 点为基础, 使广泛使用的层点云结构( LPC) 变异, 用于 LOD 制作和流传。 GPU 提供的大规模性能改进使我们通过彩色过滤提高低 LOD 质量, 同时与非过滤的、 CPU 的艺术状态相比, 仍然在提高建筑速度。 背景 : LOD 结构需要使数亿至数万个点, 但建造它们需要时间。 结果 : 适合投放和流流流的 LOD 结构以每秒约10亿点( 带色过滤) 的速度构建到每秒40亿点( 采样/ 随机取样, 艺术状态) 。 这使得我们得以通过 RTX 3090 提高 低调的, 与 CPU 的艺术状态相比, 80- 400 次( 每秒为1 200万点) 。 背景: 由于处于核心,, 模型大小限制在每24GB 记忆中约 5亿 点 。 讨论: 我们目前的方法侧重于建造中以最大核心 的建造速度为最大-,, 我们的预期的建造计划是 。</s>