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.

通过生成适应性深度平面的分区式多平面图像（TMPI），提供了一种有效的方法来合成单视图的三维摄影图像，并具有较低的计算开销。

用于实际三维摄影的平铺多平面图像

Tiled Multiplane Images for Practical 3D Photography

CodeNeRF is an implicit 3D neural representation that learns the variation of
object shapes and textures across a category and can be trained, from a set of
posed images, to synthesize novel views of unseen objects. Unlike the original
NeRF, which is scene specific, CodeNeRF learns to disentangle shape and texture
by learning separate embeddings. At test time, given a single unposed image of
an unseen object, CodeNeRF jointly estimates camera viewpoint, and shape and
appearance codes via optimization. Unseen objects can be reconstructed from a
single image, and then rendered from new viewpoints or their shape and texture
edited by varying the latent codes. We conduct experiments on the SRN
benchmark, which show that CodeNeRF generalises well to unseen objects and
achieves on-par performance with methods that require known camera pose at test
time. Our results on real-world images demonstrate that CodeNeRF can bridge the
sim-to-real gap. Project page: https://github.com/wayne1123/code-nerf

CodeNeRF 利用 3D 神经表征来学习物体的形状和纹理变化，从而可以合成未知物体的新视角，并且可以通过优化来估计摄像机视角、形状和外观代码。