An adaptive quadtree index reconstruction algorithm for massive 3D city models

Abstract

Oblique photogrammetry models are organized in an index tree which incorporates the concept of Hierarchical Level of Detail (HLOD) for optimal rendering of spatial data in the widely used 3D Tiles specification. The inefficient data structure of the index tree, such as gird, may cause problems such as rendering delay and memory overflow on the client-side. This paper proposes an adaptive quadtree index reconstruction algorithm to construct an upper quadtree filled with virtual nodes for tiled tiles of a single oblique photogrammetry dataset using the named coordinates and the actual geographic coordinates of the tiles. The improved structure enables fast elimination of invisible tiles by exploiting the visibility relationship between parent and child nodes, thus improving the traversal rate of tiles. Experiments prove that the algorithm is effective in improving the loading efficiency of local tiles in a single tileset as well as tiles at the junction of multiple tilesets, which is applicable to most browsing scenarios. In addition, the algorithm minimizes the negative impact of the adaptive quadtree structure when observing panoramic tiles by adequately balancing the number of virtual nodes, the depth of the quadtree and the efficiency of tile culling, and experiments demonstrate that the time consumption is within an acceptable range, and the negative impact is insufficient to offset the positive effect of the performance enhancement.