论文标题
用多层图像的立体视频放大
Stereo Magnification with Multi-Layer Images
论文作者
论文摘要
用多个半透明的彩色层代表场景一直是实时小说综合的流行和成功的选择。现有方法推断出平面或球形形状的规则间隔层上的颜色和透明度值。在这项工作中，我们基于具有场景适应几何形状的多个半透明层引入了一种新的视图合成方法。我们的方法在两个阶段中从立体对中提出了这些表示形式。第一阶段会从给定的视图中吸收少数数据自适应层的几何形状。第二阶段渗透了这些层的颜色和透明度值，从而产生了新型视图合成的最终表示形式。重要的是，两个阶段都是通过可区分的渲染器连接的，并以端到端的方式进行了训练。在实验中，我们证明了所提出的方法比使用定期间隔的层没有适应场景几何形状的优势。尽管在渲染过程中的数量级更快，但我们的方法还胜过基于隐式几何表示的最近提出的IBRNET系统。请参阅https://samsunglabs.github.io/stereolayers的结果。
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 .
