Sentence examples for manifold lifting viewpoint from inspiring English sources

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.

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

Indeed, one cannot disregard the role that concise models such as sparsity may still play in an effective manifold lifting algorithm.

Perhaps surprisingly, the reconstruction quality is actually inferior to that obtained using the manifold lifting algorithm with distributed measurements (shown in Figure 8c).

However, when we concatenate the ensemble of encoded wavelet coefficients and solve (1) to estimate x ^, we see from the result in Figure 8b that the reconstructed image has lower quality than that we obtained using a manifold lifting algorithm based on random measurements, even though the camera positions were unknown for the manifold lifting experiment.

Figure 7 Reconstruction results in manifold lifting demonstration.

Figure 8 Comparative reconstructions for manifold lifting demonstration.

We conclude with a few remarks concerning practical and theoretical aspects of the manifold lifting framework.

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

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