Photometric stereo leverages variations in illumination conditions to
reconstruct per-pixel surface normals. The concept of display photometric
stereo, which employs a conventional monitor as an illumination source, has the
potential to overcome limitations often encountered in bulky and
difficult-to-use conventional setups. In this paper, we introduce
Differentiable Display Photometric Stereo (DDPS), a method designed to achieve
high-fidelity normal reconstruction using an off-the-shelf monitor and camera.
DDPS addresses a critical yet often neglected challenge in photometric stereo:
the optimization of display patterns for enhanced normal reconstruction. We
present a differentiable framework that couples basis-illumination image
formation with a photometric-stereo reconstruction method. This facilitates the
learning of display patterns that leads to high-quality normal reconstruction
through automatic differentiation. Addressing the synthetic-real domain gap
inherent in end-to-end optimization, we propose the use of a real-world
photometric-stereo training dataset composed of 3D-printed objects. Moreover,
to reduce the ill-posed nature of photometric stereo, we exploit the linearly
polarized light emitted from the monitor to optically separate diffuse and
specular reflections in the captured images. We demonstrate that DDPS allows
for learning display patterns optimized for a target configuration and is
robust to initialization. We assess DDPS on 3D-printed objects with
ground-truth normals and diverse real-world objects, validating that DDPS
enables effective photometric-stereo reconstruction.

本文介绍了 Differentiable Display Photometric Stereo (DDPS)，使用一个商用显示器和相机实现高保真度的表面法线重建。利用线性偏振光，DDPS 实现了漫反射和镜面反射的分离。该方法通过自动微分学习实现了显示模式的优化，且在验证中表现出了良好的稳健性。