Practical Time Bundle Adjustment for 3D Reconstruction on the GPU
Abstract
Large-scale 3D reconstruction has received a lot of attention recently. Bundle adjustment is a key component of the reconstruction pipeline and often its slowest and most computational resource intensive. It hasn’t been parallelized effectively so far. In this paper, we present a hybrid implementation of sparse bundle adjustment on the GPU using CUDA, with the CPU working in parallel. The algorithm is decomposed into smaller steps, each of which is scheduled on the GPU or the CPU. We develop efficient kernels for the steps and make use of existing libraries for several steps. Our implementation outperforms the CPU implementation significantly, achieving a speedup of 30-40 times over the standard CPU implementation for datasets with upto 500 images on an Nvidia Tesla C2050 GPU.
Cite
Text
Choudhary et al. "Practical Time Bundle Adjustment for 3D Reconstruction on the GPU." European Conference on Computer Vision, 2010. doi:10.1007/978-3-642-35740-4_33Markdown
[Choudhary et al. "Practical Time Bundle Adjustment for 3D Reconstruction on the GPU." European Conference on Computer Vision, 2010.](https://mlanthology.org/eccv/2010/choudhary2010eccv-practical/) doi:10.1007/978-3-642-35740-4_33BibTeX
@inproceedings{choudhary2010eccv-practical,
title = {{Practical Time Bundle Adjustment for 3D Reconstruction on the GPU}},
author = {Choudhary, Siddharth and Gupta, Shubham and Narayanan, P. J.},
booktitle = {European Conference on Computer Vision},
year = {2010},
pages = {423-435},
doi = {10.1007/978-3-642-35740-4_33},
url = {https://mlanthology.org/eccv/2010/choudhary2010eccv-practical/}
}