  We study the inverse graphics problem of inferring a holistic representation
for natural images. Given an input image, our goal is to induce a
neuro-symbolic, program-like representation that jointly models camera poses,
object locations, and global scene structures. Such high-level, holistic scene
representations further facilitate low-level image manipulation tasks such as
inpainting. We formulate this problem as jointly finding the camera pose and
scene structure that best describe the input image. The benefits of such joint
inference are two-fold: scene regularity serves as a new cue for perspective
correction, and in turn, correct perspective correction leads to a simplified
scene structure, similar to how the correct shape leads to the most regular
texture in shape from texture. Our proposed framework, Perspective Plane
Program Induction (P3I), combines search-based and gradient-based algorithms to
efficiently solve the problem. P3I outperforms a set of baselines on a
collection of Internet images, across tasks including camera pose estimation,
global structure inference, and down-stream image manipulation tasks.
