RI: Small: Learning-Based Systems for Single-Image Photometric Reconstruction

该项目侧重于开发算法和数据集，将光度重建系统从手工设计的系统转换为基于学习的系统，并根据现实世界的数据进行优化。光度重建系统从表面上不同位置的感知强度中获得线索。形状-从阴影，其中表面被假定具有漫反射，是光度重建的一个众所周知的例子。该项目产生了使用机器学习技术建立光度重建模型所需的数据集和方法。这种基于学习的方法使系统能够根据实际数据进行优化，从而产生最准确的结果。此外，这种基于学习的方法能够开发出比手工设计系统中通常使用的参数更多的更复杂的方法。以自动化方式找到最佳参数的能力不仅可以改进现有方法，例如更有效地合并图像数据，而且还可以推动算法的发展，突破当前系统的界限。特别是，在不知道照明或甚至试图明确建模的情况下，开发了用于估计物体形状的算法。如果没有训练和测试数据，学习方法的力量是无法实现的。这项工作的主要任务是构建图像数据库和图像中物体的地面真实三维重建。使用基于实例的光度立体技术可以找到三维模型。

This project focuses on developing algorithms and datasets that can transform photometric reconstruction systems from hand-designed systems into learning-based systems that are optimized on real-world data. Photometric reconstruction systems derive cues from the perceived intensity of different locations on a surface. Shape-from-shading, where the surface is assumed to have a diffuse reflectance, is a well-known example of photometric reconstruction. This project produces the datasets and methods necessary to use machine learning techniques to build models for photometric reconstruction.This learning-based approach enables systems to be optimized on real-world data so that they produce the most accurate results possible. In addition, this learning-based approach enables the development of more sophisticated methods with more parameters than typically used in hand-designed systems. The ability to find optimal parameters in an automated fashion can not only improve existing approaches, such as by incorporating image data more effectively, but can also enable the development of algorithms that push the boundaries of current systems. In particular, algorithms are developed for estimating the shape of objects without knowing the illumination or even trying to explicitly model it.The power of the learning approach cannot be realized without data for training and testing. A major task in this work is the construction of a database of images and ground-truth 3D reconstructions of the objects in the images. The 3D models can be found using an example-based photometric stereo technique.