A Solution for a Fundamental Problem of 3D Inference based on 2D Representations

TL;DR

This paper introduces a new fundamental problem in 3D inference from 2D representations, proposing an explainable gradient-based solution that enhances understanding and robustness in 3D object pose estimation from monocular images.

Contribution

It defines a generalized 3D inference problem linked to 2D representations and offers a robust, explainable gradient descent method for a key special case, advancing 3D pose estimation techniques.

Findings

Provides a new formulation of the fundamental 3D inference problem

Develops a gradient-based solution for a special case of the problem

Enhances robustness and explainability in 3D pose estimation from 2D images

Abstract

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.