论文标题
基于2D表示的3D推理基本问题的解决方案
A Solution for a Fundamental Problem of 3D Inference based on 2D Representations
论文作者
论文摘要
使用神经网络从单眼视力中的3D推断是计算机视觉的重要研究领域。研究领域的应用与许多提出的解决方案有关，并且表现出色。尽管已经投入了许多努力，但仍有未解决的问题，其中一些是基本的。在本文中，我讨论了一个我希望将其称为基于2D表示的对象驱动的3D推断的盲目视角n点（盲点PNP）问题的问题。基本问题和盲目PNP问题之间的重要差异是，基本问题中的3D推理参数直接附加到3D点，并且将通过共享这些点的参数来表示相机概念。通过为问题的重要特殊情况提供一种基于2D表示的可解释且健壮的梯度定位解决方案，该论文开辟了一种新方法，用于使用可用的基于信息的学习方法来解决与2D图像中与3D对象姿势估计相关的问题。
3D inference from monocular vision using neural networks is an important research area of computer vision. Applications of the research area are various with many proposed solutions and have shown remarkable performance. Although many efforts have been invested, there are still unanswered questions, some of which are fundamental. In this paper, I discuss a problem that I hope will come to be known as a generalization of the Blind Perspective-n-Point (Blind PnP) problem for object-driven 3D inference based on 2D representations. The vital difference between the fundamental problem and the Blind PnP problem is that 3D inference parameters in the fundamental problem are attached directly to 3D points and the camera concept will be represented through the sharing of the parameters of these points. By providing an explainable and robust gradient-decent solution based on 2D representations for an important special case of the problem, the paper opens up a new approach for using available information-based learning methods to solve problems related to 3D object pose estimation from 2D images.
