What is it about?
The paper proposes a variational Bayes, multi-resolution method for two-frame structure from motion (SfM). The method jointly estimates depth and camera motion. It accomplishes this by fitting the brightness-constancy gradient with a perspective flow model. It also updates the time derivative via image warping and propagates posteriors across scales. This process suppresses aliasing and captures discontinuities.
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Why is it important?
It improves the accuracy and robustness of two-frame poses. Hierarchical inference reliably carries low-resolution estimates upward to avoid aliasing and recover fine structure. Real-image tests show that naive maximum likelihood estimation (MLE) or no warping largely fails, highlighting the gains and a path toward temporal integration.
Perspectives
Extend the two-frame VB multi-resolution scheme to temporal sequences using Kalman-like online integration/bundle adjustment to achieve greater accuracy. Then, reapply VB-EM globally with image warping, refine noise sharing across layers, and benchmark against robust statistics SOTA. Finally, integrate low-variance motion estimators to curb depth of field (DoF) effects.
Norio Tagawa
Read the Original
This page is a summary of: Structure from Motion with Variational Bayesian Inference in Multi-resolution Networks, December 2024, Springer Science + Business Media,
DOI: 10.1007/978-3-031-78444-6_20.
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