We present a novel method for efficient acquisition of shape and spatially
varying reflectance of 3D objects using polarization cues. Unlike previous
works that have exploited polarization to estimate material or object
appearance under certain constraints (known shape or multiview acquisition), we
lift such restrictions by coupling polarization imaging with deep learning to
achieve high quality estimate of 3D object shape (surface normals and depth)
and SVBRDF using single-view polarization imaging under frontal flash
illumination. In addition to acquired polarization images, we provide our deep
network with strong novel cues related to shape and reflectance, in the form of
a normalized Stokes map and an estimate of diffuse color. We additionally
describe modifications to network architecture and training loss which provide
further qualitative improvements. We demonstrate our approach to achieve
superior results compared to recent works employing deep learning in
conjunction with flash illumination.

本文提出了一种使用偏振线索高效获取物体形状和空间变化反射的新方法，其中偏振成像与深度学习相结合，可以在正面闪光照射下使用单视角偏振成像实现高质量估计物体形状和反射。

深度极化成像应用于三维形状和 SVBRDF 获取

Deep Polarization Imaging for 3D shape and SVBRDF Acquisition

Perceiving accurate 3D object shape is important for robots to interact with
the physical world. Current research along this direction has been primarily
relying on visual observations. Vision, however useful, has inherent
limitations due to occlusions and the 2D-3D ambiguities, especially for
perception with a monocular camera. In contrast, touch gets precise local shape
information, though its efficiency for reconstructing the entire shape could be
low. In this paper, we propose a novel paradigm that efficiently perceives
accurate 3D object shape by incorporating visual and tactile observations, as
well as prior knowledge of common object shapes learned from large-scale shape
repositories. We use vision first, applying neural networks with learned shape
priors to predict an object's 3D shape from a single-view color image. We then
use tactile sensing to refine the shape; the robot actively touches the object
regions where the visual prediction has high uncertainty. Our method
efficiently builds the 3D shape of common objects from a color image and a
small number of tactile explorations (around 10). Our setup is easy to apply
and has potentials to help robots better perform grasping or manipulation tasks
on real-world objects.

通过融合视觉和触觉观测以及来自大规模形状数据库的先验知识，我们提出了一种高效的模式，从单视角彩色图像和少量触索中构建常见物体的三维形状，以帮助机器人更好地执行实际操作。