The use of rendered images, whether from completely synthetic datasets or
from 3D reconstructions, is increasingly prevalent in vision tasks. However,
little attention has been given to how the selection of viewpoints affects the
performance of rendered training sets. In this paper, we propose a data-driven
approach to view set selection. Given a set of example images, we extract
statistics describing their contents and generate a set of views matching the
distribution of those statistics. Motivated by semantic segmentation tasks, we
model the spatial distribution of each semantic object category within an image
view volume. We provide a search algorithm that generates a sampling of likely
candidate views according to the example distribution, and a set selection
algorithm that chooses a subset of the candidates that jointly cover the
example distribution. Results of experiments with these algorithms on SUNCG
indicate that they are indeed able to produce view distributions similar to an
example set from NYUDv2 according to the earth mover's distance. Furthermore,
the selected views improve performance on semantic segmentation compared to
alternative view selection algorithms.

本文提出了一种基于数据驱动方法的视图集合选择算法，通过建模图像视图体积内每个语义对象类别的空间分布，生成可能候选视图样本的采样和选择视图集合的算法，通过 SUNCG 上的实验表明，该算法能够根据示例分布生成与 NYUDv2 的相似视图分布，选定的视图与替代视图选择算法相比，能够提高语义分割性能。