The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Multicore bundle adjustment
Abstract
We present the design and implementation of new inexact Newton type Bundle Adjustment algorithms that exploit hardware parallelism for efficiently solving large scale 3D scene reconstruction problems. We explore the use of multicore CPU as well as multicore GPUs for this purpose. We show that overcoming the severe memory and bandwidth limitations of current generation GPUs not only leads to more space efficient algorithms, but also to surprising savings in runtime. Our CPU based system is up to ten times and our GPU based system is up to thirty times faster than the current state of the art methods, while maintaining comparable convergence behavior. The code and additional results are available at http://grail.cs.washington.edu/projects/mcba.
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Cites
Bundle Adjustment — A Modern Synthesis
Triggs, B., McLauchlan, P. F., Hartley, R. I., & Fitzgibbon, A. W. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44480-7_21 · Cited by 3,988
Trefethen, L. N., & Bau, III (1997) · doi:10.1137/1.9780898719574 · Cited by 3,301
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Walch, F., Hazirbas, C., Leal-Taixe, L., Sattler, T., Hilsenbeck, S., & Cremers, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.75 · Cited by 521
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Self-citation
Metadata
- Authors
- Wu, C., Agarwal, S., Curless, B., & Seitz, S. M.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 3057-3064
- OpenAlex
- W2001790138
- Cited by
- 807
- References
- 2
- FWCI
- 36.11
- Type
- conference paper
- Language
- en
- Open access
- closed
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