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Related Experiment Video

Updated: May 20, 2026

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

Pushing the envelope of modern methods for bundle adjustment.

Yekeun Jeong¹, David Nistér, Drew Steedly

¹Microsoft Corporation, Redmond, WA 98052-6399, USA. yejeong@microsoft.com

IEEE Transactions on Pattern Analysis and Machine Intelligence

|June 30, 2012

Summary

We developed the fastest bundle adjustment methods for computer vision, leveraging block-sparse patterns for computational efficiency. Novel embedded point iterations further accelerate convergence, improving 3D reconstruction accuracy.

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Area of Science:

Computer Vision
Robotics
Computational Geometry

Background:

Bundle adjustment is crucial for optimizing 3D scene reconstructions.
Existing methods face computational bottlenecks, limiting scalability.
Efficiently handling sparse structures is key to performance.

Purpose of the Study:

To present novel, highly efficient bundle adjustment algorithms.
To demonstrate superior computational speed and convergence rates.
To improve the accuracy and speed of 3D scene reconstruction.

Main Methods:

Exploiting block-sparse patterns in reduced camera systems.
Implementing optimized linear algebra (BLAS3) and sparse solvers.
Utilizing exact minimum degree ordering with block-based LDL decomposition.
Employing block-based preconditioned conjugate gradients.
Introducing embedded point iterations within camera update steps.

Main Results:

Achieved the fastest bundle adjustment computation and convergence published.
Demonstrated significant performance improvements over previous methods.
Validated enhanced speed and accuracy through extensive experiments.
Showcased the effectiveness of block-sparsity adaptation and embedded iterations.

Conclusions:

The proposed methods offer state-of-the-art performance in bundle adjustment.
Block-sparsity exploitation and embedded iterations are critical for efficiency.
These advancements enable faster and more accurate 3D reconstructions.