Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold

Understand

Single image pose estimation is a fundamental problem in many vision and robotics tasks, and existing deep learning approaches suffer by not completely modeling and handling: i) uncertainty about the predictions, and ii) symmetric objects with multiple (sometimes infinite) correct poses.

• To this end, we introduce a method to estimate arbitrary, non-parametric distributions on SO(3).
• Our key idea is to represent the distributions implicitly, with a neural network that estimates the probability given the input image and a candidate pose.
• Grid sampling or gradient ascent can be used to find the most likely pose, but it is also possible to evaluate the probability at any pose, enabling reasoning about symmetries and uncertainty.