Recent novel view synthesis methods obtain promising results for relatively
small scenes, e.g., indoor environments and scenes with a few objects, but tend
to fail for unbounded outdoor scenes with a single image as input. In this
paper, we introduce SAMPLING, a Scene-adaptive Hierarchical Multiplane Images
Representation for Novel View Synthesis from a Single Image based on improved
multiplane images (MPI). Observing that depth distribution varies significantly
for unbounded outdoor scenes, we employ an adaptive-bins strategy for MPI to
arrange planes in accordance with each scene image. To represent intricate
geometry and multi-scale details, we further introduce a hierarchical
refinement branch, which results in high-quality synthesized novel views. Our
method demonstrates considerable performance gains in synthesizing large-scale
unbounded outdoor scenes using a single image on the KITTI dataset and
generalizes well to the unseen Tanks and Temples dataset. The code and models
will be made public.

基于改进的多平面图像（MPI），本文介绍了一种适应场景的分层多平面图像表示，用于从单幅图像合成新视角，观察到对于无界户外场景，深度分布变化显著，因此采用自适应分组策略来排列平面，进一步引入了分层细化分支，以呈现精细的几何和多尺度细节，该方法在 KITTI 数据集上合成大规模无界户外场景并具有显著的性能提升，在未见过的 Tanks and Temples 数据集上具有较好的泛化性能，代码和模型将公开。