A Solution for a Fundamental Problem of 3D Inference based on 2D Representations
This addresses a fundamental problem in computer vision for applications like 3D object pose estimation, though it appears incremental as it builds on existing Blind PnP concepts.
The paper tackles the problem of 3D inference from monocular vision by generalizing the Blind Perspective-n-Point problem for object-driven 3D inference based on 2D representations, proposing an explainable and robust gradient-descent solution that opens up a new approach for using information-based learning methods in 3D object pose estimation.
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.