Deep Blending for Free-Viewpoint Image-Based Rendering

International audienceIBR Algorithm Real surface Reconstruction Fig. 1. Image-based rendering blends contributions from different input images to synthesize a novel view. (a) This blending operation is complex for a variety of reasons. For example, incorrect visibility (e.g., for the non-reconstructed green surface in input view (1)) may result in wrong projections of an image into the novel view. Blending must also account for, e.g., differences in projected resolution ((2) and (3)). Previous methods have used hand-crafted heuristics to overcome these issues. (b) Our method generates a set of ranked contributions (mosaics) from the input images and uses predicted blending weights from a CNN to perform deep blending and synthesize (c) novel views. Our solution significantly reduces visible artifacts compared to (d) previous methods. Free-viewpoint image-based rendering (IBR) is a standing challenge. IBR methods combine warped versions of input photos to synthesize a novel view. The image quality of this combination is directly affected by geometric inaccuracies of multi-view stereo (MVS) reconstruction and by view-and image-dependent effects that produce artifacts when contributions from different input views are blended. We present a new deep learning approach to blending for IBR, in which we use held-out real image data to learn blending weights to combine input photo contributions. Our Deep Blending method requires us to address several challenges to achieve our goal of interactive free-viewpoint IBR navigation. We first need to provide sufficiently accurate geometry so the Convolutional Neural Network (CNN) can succeed in finding correct blending weights. We do this by combining two different MVS reconstructions with complementary accuracy vs. completeness tradeoffs. To tightly integrate learning in an interactive IBR system, we need to adapt our rendering algorithm to produce a fixed number of input layers that can then be blended by the CNN. We generate training data with a variety of captured scenes, using each input photo as ground truth in a held-out approach. We also design the network architecture and the training loss to provide high quality novel view synthesis, while reducing temporal flickering artifacts. Our results demonstrate free-viewpoint IBR in a wide variety of scenes, clearly surpassing previous methods in visual quality, especially when moving far from the input cameras

Electronegativity of -SF₅, -CF₃SO₂, and -SO₂F mmeasured with X-ray photoelectron spectroscopy

The electronegativities of -SF₅, -CF₃SO₂, and -SO₂F have been measured with gas phase X-ray photoelectron spectroscopy. Of these three groups previously measured values were found only for -SF₅. The electronegativities of -Cl, -Br, -CF₃, and -CF₃, were concurrently measured to determine how the findings of this research compare with other experimental values found in the literature. Our experimental electronegativities for -SF₅, -CF₃SO₂, and -SO₂F are 2.81 ± 0.12, 2.49 ± 0.09, and 2.92 ± 0.12 respectively. The electronegativity of -CF₃, currently measured (2.78 ± 0.08) is significantly lower than other experimental values. The electronegativity of -SF₅ is in agreement with other experimental values. The experimental electronegativities are compared with theoretical values calculated based on the assumption of Electronegativity Equalization and based on Mulliken's definition of electronegativity. It is found that the electronegativities calculated with Electronegativity Equalization are higher than the experimental values currently measured, and those calculated with Mulliken's definition are lower than the experimental values