Multicore Bundle Adjustment

The emergence of multi-core computers represents a fundamental shift, with major implications for the design of computer vision algorithms. Most computers sold today have a multicore CPU with 2-16 cores and a GPU with anywhere from 4 to 128 cores. Exploiting this hardware parallelism will be key to the success and scalability of computer vision algorithms in the future. In this project, we consider 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.

Paper

Changchang Wu, Sameer Agarwal, Brian Curless, and Steven M. Seitz, "Multicore Bundle Adjustment", CVPR 2011, (poster ,supplemental material)

Software (written and maintained by Changchang Wu)

pba(v1.0.4) (1MB, code and win32 binary, updated on 12/05/2011) manual.pdf(8/01/2011, a short usage manual) VisualSFM, an integrated SFM software you may be interested in. The software is distributed under the GNU General Public License V3. For commercial licensing of the software, please contact Changchang Wu.

Recent changes (complete changelist, previous versions)

12/05/2011    Added first version of Intel-AVX-based implementation for CPU 8/19/2011    Supporting per-camera constant flag (CameraT::SetConstantCamera)    Automatic switching from CUDA to CPU if CUDA is not supported 8/01/2011    CPU thread count adjustment according to the number of cores;    Added Motion-only and Structure-only bundle adjustment mode. 6/30/2011    Added first draft of manual;    Included the CMakeLists provided by Pierre Moulon.

Performance tuning for CPU implementation

The thread number settings are coded in function:              SparseBundleCPU::SparseBundleCPU() You can tune the thread count to get better performance. If you run "driver filename -profile --float", the program will print out some useful function timing.

Data

The data used in the paper are from the Bundle Adjustment in the Large project. The only exception is the slightly larger Venice Final model. Some additional test data from UNC Landmark image reconstruction projects can be found here.

Documentation

A short manual is available here, and more will come ...