Octree is a hierarchical data structure with many applications, especially in encoding unstructured point clouds. The depth of an octree is dependent of the scale of the input data and the desired resolution of the smallest voxels in the leaf nodes as well. Thus, it often requires a deep octree to maintain low level of geometric errors for large-scale sparse point clouds, which leads to high memory requirement and low access speed. This paper presents a new structure called truncated octree or T-Octree that truncates the octree by adaptively pruning the top hierarchy and represents the deep octree by a set of shallow sub-octrees. The structure is further extended to support random access of nodes and out-of-core streaming of large data sets. We also propose a variable length addressing scheme to adaptively choose the length of an octree’s node address based on the truncation level. As a result, T-Octree provides highly efficient query performance and can save storage without losing the original structure for sparse or clustered models and scenes. We demonstrate the efficacy and efficiency of the new structure on point cloud compression and scene query tasks for sparse or clustered data.

Octree是具有许多应用程序的分层数据结构，尤其是在编码非结构化点云中。八叉树的深度取决于输入数据的大小以及叶节点中最小体素的所需分辨率。因此，对于大型稀疏点云，经常需要使用深层八叉树来维持较低水平的几何错误，这会导致较高的内存需求和较低的访问速度。本文提出了一种称为截短八叉树或T-八叉树的新结构通过自适应修剪顶层层次结构来截断八叉树，并通过一组浅子八叉树来表示深八叉树。该结构进一步扩展为支持节点的随机访问和大数据集的核外流传输。我们还提出了一种可变长度寻址方案，以根据截断级别自适应地选择八叉树节点地址的长度。结果，T-Octree提供了高效的查询性能，并且可以节省存储空间而不会丢失稀疏或群集模型和场景的原始结构。我们展示了新结构在稀疏或群集数据的点云压缩和场景查询任务上的功效和效率。