A v i d i a 2k a is t Jan Kautz1 Zhou at al. Ours f 30 x Extrapolated Left uput Cameras Extrapolated Right Figure 1: We propose a novel view synthesis method that can generate extreme views, i.e., images synthesized from a small number of cameras (two in this example) and from significantly different viewpoints. In this comparison with the method by Zhou et al. [34], we show the left view from the camera setup depicted above. Even at a 30 x baseline magnification our method produces sharper results. We present Extreme View Synthesis, a solution fo r novel view extrapolation that works even when the number of in put images is small-as few as two. In this context, occlu sions and depth uncertainty are two of the most pressing issues, and worsen as the degree of extrapolation increases. We follow the traditional paradigm o f performing depthbased warping and refinement, with a few key improve ments. First, we estimate a depth probability volume, rather than just a single depth value fo r each pixel of the novel view. This allows us to leverage depth uncertainly in chal lenging regions, such as depth discontinuities. After using it to get an initial estimate of the novel view, we explicitly combine learned image priors and the depth uncertainty to synthesize a refined image with less artifacts. Our method is the first to show visually pleasing results fo r baseline mag nifications of up to 30 x. The code is available at h t t p s : / / g i t h u b . c o m /N V la b s /e x tre m e -v ie w -s y n th
Recently, Choi et al. REF use deep neural network to estimate a depth probability volume, rather than just a single depth value for each pixel of the novel view.
Extreme View Synthesis
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Computer Science