Sentence examples for manifold lifting algorithm from inspiring English sources

Figure 6c shows the final estimated camera positions after all iterations of our manifold lifting algorithm.

c Joint reconstruction using our manifold lifting algorithm with unknown camera positions, PSNR 23.6 dB.

The goal of a manifold lifting algorithm is to recover an ensemble of images from their low-dimensional measurements.

b Initial estimates { θ j ^ } of camera positions after rotating and scaling the {v j }. c Final camera position estimates after running the manifold lifting algorithm.

In all cases, our manifold lifting algorithm without knowledge of the camera positions outperforms transform coding with knowledge of the camera positions.

We divide our discussion into two scenarios, the near-field and far-field cases, and describe how the manifold lifting algorithm could be applied to these scenarios.

We hope that such discussions will pave the way for the future development of broader classes of manifold lifting algorithms.

We have presented a geometric framework in which many multi-view imaging problems may be cast and explained how this framework can inform the design of effective manifold lifting algorithms for joint reconstruction.

As a proof of concept, we now present a comprehensive multi-view reconstruction algorithm inspired by the manifold lifting viewpoint.

To make things more concrete, we demonstrate in this section how the manifold lifting viewpoint can inform the design of reconstruction algorithms in the context of two generic multi-view scenarios: far-field and near-field imaging.

To inform the design of such an algorithm, we find it helpful to view the general task of reconstruction as what we term a manifold lifting problem: we would like to recover each image x j ∈ ℝ N from its measurements y j ∈ ℝ M j ("lifting" it from the low-dimensional measurement space back to the high-dimensional signal space), while ensuring that all recovered images live along a common IAM.