The task of synthesizing novel views from a single image has useful applications in virtual reality and mobile computing, and a number of approaches to the problem have been proposed in recent years. A Multiplane Image (MPI) estimates the scene as a stack of RGBA layers, and can model complex appearance effects, anti-alias depth errors and synthesize soft edges better than methods that use textured meshes or layered depth images. And unlike neural radiance fields, an MPI can be efficiently rendered on graphics hardware. However, MPIs are highly redundant and require a large number of depth layers to achieve plausible results. Based on the observation that the depth complexity in local image regions is lower than that over the entire image, we split an MPI into many small, tiled regions, each with only a few depth planes. We call this representation a Tiled Multiplane Image (TMPI). We propose a method for generating a TMPI with adaptive depth planes for single-view 3D photography in the wild. Our synthesized results are comparable to state-of-the-art single-view MPI methods while having lower computational overhead.

翻译：从单张图像合成新视角的任务在虚拟现实和移动计算中具有重要应用价值，近年来研究者提出了多种解决方案。叠层图像（MPI）通过将场景表示为RGBA层的堆栈，能够建模复杂外观效果、抗锯齿深度误差，并比基于纹理网格或分层深度图像的方法合成出更柔和的边缘。与神经辐射场不同，MPI可在图形硬件上高效渲染。然而，MPI存在高度冗余问题，需要大量深度层才能获得合理结果。基于局部图像区域的深度复杂度低于全局图像这一观察，我们将MPI拆解为众多小平铺区域，每个区域仅包含少量深度平面，这种表示称为平铺叠层图像（TMPI）。我们提出了一种自适应深度平面生成TMPI的方法，用于真实场景下的单视角3D摄影。实验结果表明，本方法在计算开销更低的前提下，达到了与当前最先进单视角MPI方法相媲美的合成效果。