Object reconstruction from a single image -- in the wild -- is a problem
where we can make progress and get meaningful results today. This is the main
message of this paper, which introduces an automated pipeline with pixels as
inputs and 3D surfaces of various rigid categories as outputs in images of
realistic scenes. At the core of our approach are deformable 3D models that can
be learned from 2D annotations available in existing object detection datasets,
that can be driven by noisy automatic object segmentations and which we
complement with a bottom-up module for recovering high-frequency shape details.
We perform a comprehensive quantitative analysis and ablation study of our
approach using the recently introduced PASCAL 3D+ dataset and show very
encouraging automatic reconstructions on PASCAL VOC.

本文介绍了一种基于像素输入和 3D 刚性类别的输出的自动化流程，通过从现有物体检测数据集中学习可变形的 3D 模型并结合底层模块进行高频率形状细节恢复，实现了在单幅图像中获取真实场景下物体的三维表面重建，通过在最近引入的 PASCAL 3D + 数据集上的全面定量分析和消融研究展示了非常令人鼓舞的自动重建效果。