Trilaminar Multiway Reconstruction Tree for Efficient Large Scale Structure from Motion
This addresses efficiency and accuracy issues in large-scale 3D reconstruction for computer vision applications, but it appears incremental as it builds on existing SfM methods with optimizations.
The paper tackles the problem of improving accuracy and efficiency in large-scale incremental Structure from Motion by proposing a unified framework that clusters images for reconstruction and finds multiple starting points, resulting in significant speedup, higher accuracy, and better completeness.
Accuracy and efficiency are two key problems in large scale incremental Structure from Motion (SfM). In this paper, we propose a unified framework to divide the image set into clusters suitable for reconstruction as well as find multiple reliable and stable starting points. Image partitioning performs in two steps. First, some small image groups are selected at places with high image density, and then all the images are clustered according to their optimal reconstruction paths to these image groups. This promises that the scene is always reconstructed from dense places to sparse areas, which can reduce error accumulation when images have weak overlap. To enable faster speed, images outside the selected group in each cluster are further divided to achieve a greater degree of parallelism. Experiments show that our method achieves significant speedup, higher accuracy and better completeness.