  Representing scenes with multiple semi-transparent colored layers has been a
popular and successful choice for real-time novel view synthesis. Existing
approaches infer colors and transparency values over regularly-spaced layers of
planar or spherical shape. In this work, we introduce a new view synthesis
approach based on multiple semi-transparent layers with scene-adapted geometry.
Our approach infers such representations from stereo pairs in two stages. The
first stage infers the geometry of a small number of data-adaptive layers from
a given pair of views. The second stage infers the color and the transparency
values for these layers producing the final representation for novel view
synthesis. Importantly, both stages are connected through a differentiable
renderer and are trained in an end-to-end manner. In the experiments, we
demonstrate the advantage of the proposed approach over the use of
regularly-spaced layers with no adaptation to scene geometry. Despite being
orders of magnitude faster during rendering, our approach also outperforms a
recently proposed IBRNet system based on implicit geometry representation. See
results at https://samsunglabs.github.io/StereoLayers .
