Scalable point cloud meshing for image-based large-scale 3D modeling

A Parallel Method for Texture Reconstruction in Large-Scale 3D Automatic Modeling Based on Oblique Photography

Common methods of texture reconstruction first build a visual list for each triangular face, and then select the best image for each triangular face based on the graph-cut method. These methods have problems such as high memory consumption, and difficulties in large-area texture reconstruction. Hence, this paper proposes a parallel method for texture reconstruction in large-scale 3D automatic modeling. First, the hierarchical relationships between the texture reconstruction are calculated in accordance with the adjacency relationships between partitioning cells. Second, building contours are extracted based on the 3D mesh model, the tiles are divided into two categories (occlusion and non-occlusion), and the incorrect occlusion relationship is restored based on the occluded tiles. Then, the graph-cut algorithm is constructed to select the best-view label. Finally, the jagged labels between adjacent labels are smoothed to alleviate the problem of texture seams. Oblique photography data from an area of 10 km2 in Dongying, Shandong were used for validation. The experimental results reveal the following: (i) concerning reconstruction efficiency, the Waechter method can perform texture reconstruction only in a small area, whereas with the proposed method, the size of the reconstruction area is not restricted. The memory consumption is improved by factors of approximately 2–13. (ii) Concerning reconstruction results, the Waechter method incorrectly reconstructs the textures of partially occluded regions at the tile edges, while the proposed method can reconstruct the textures correctly. (iii) Compared to the Waechter method, the proposed approach has a 30% lower reduction in the number of texture fragments.

A Parallel Method for Open Hole Filling in Large-Scale 3D Automatic Modeling Based on Oblique Photography

Common methods of filling open holes first reaggregate them into closed holes and then use a closed hole filling method to repair them. These methods have problems such as long calculation times, high memory consumption, and difficulties in filling large-area open holes. Hence, this paper proposes a parallel method for open hole filling in large-scale 3D automatic modeling. First, open holes are automatically identified and divided into two categories (internal and external). Second, the hierarchical relationships between the open holes are calculated in accordance with the adjacency relationships between partitioning cells, and the open holes are filled through propagation from the outer level to the inner level with topological closure and height projection transformation. Finally, the common boundaries between adjacent open holes are smoothed based on the Laplacian algorithm to achieve natural transitions between partitioning cells. Oblique photography data from an area of 28 km2 in Dongying, Shandong, were used for validation. The experimental results reveal the following: (i) Compared to the Han method, the proposed approach has a 12.4% higher filling success rate for internal open holes and increases the filling success rate for external open holes from 0% to 100%. (ii) Concerning filling efficiency, the Han method can achieve hole filling only in a small area, whereas with the proposed method, the size of the reconstruction area is not restricted. The time and memory consumption are improved by factors of approximately 4–5 and 7–21, respectively. (iii) In terms of filling accuracy, the two methods are basically the same.