  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.
