About: We propose an incremental LOD generation approach for point clouds that allows us to simultaneously load points from disk, update an octree-based level-of-detail representation, and render the intermediate results in real time while additional points are still being loaded from disk. LOD construction and rendering are both implemented in CUDA and share the GPU's processing power, but each incremental update is lightweight enough to leave enough time to maintain real-time frame rates. Background: LOD construction is typically implemented as a preprocessing step that requires users to wait before they are able to view the results in real time. This approach allows users to view intermediate results right away. Results: Our approach is able to stream points from an SSD and update the octree on the GPU at rates of up to 580 million points per second (~9.3GB/s from a PCIe 5.0 SSD) on an RTX 4090. Depending on the data set, our approach spends an average of about 1 to 2 ms to incrementally insert 1 million points into the octree, allowing us to insert several million points per frame into the LOD structure and render the intermediate results within the same frame. Discussion/Limitations: We aim to provide near-instant, real-time visualization of large data sets without preprocessing. Out-of-core processing of arbitrarily large data sets and color-filtering for higher-quality LODs are subject to future work.

翻译：我们提出了一种针对点云的增量LOD生成方法，能够实现从磁盘加载点数据、更新基于八叉树的细节层次表示，以及在新点数据持续加载过程中实时渲染中间结果。LOD构建与渲染均在CUDA中实现并共享GPU算力，但每次增量更新的计算量极低，足以维持实时帧率。传统LOD构建通常需要用户等待预处理完成后才能实时查看结果，而本方法允许用户立即查看中间结果。实验表明，该方法能在RTX 4090上以每秒5.8亿个点（约9.3GB/s，基于PCIe 5.0 SSD）的速度从SSD流式传输点数据并更新GPU端八叉树。根据数据集不同，本方法平均需约1-2毫秒增量插入100万个点至八叉树，从而可在单帧内将数百万个点插入LOD结构并渲染中间结果。本方法旨在无需预处理的条件下实现大型数据集的近乎即时的实时可视化。未来工作将涉及超大型数据集的外存处理以及基于颜色滤波的更高品质LOD生成。