COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images
This work addresses the need for reliable quality assessment in object pose estimation, which is crucial for applications like robotics and augmented reality, but it is incremental as it builds on existing shape representation techniques.
The authors tackled the problem of assessing the quality of 6D object pose estimates from single images by proposing a confidence score based on shape regression analysis, resulting in a method-independent quality measure that uses Gaussian Processes as a shape template to evaluate discrepancies in geometry.
We propose a generic procedure for assessing 6D object pose estimates. Our approach relies on the evaluation of discrepancies in the geometry of the observed object, in particular its respective estimated back-projection in 3D, against a putative functional shape representation comprising mixtures of Gaussian Processes, that act as a template. Each Gaussian Process is trained to yield a fragment of the object's surface in a radial fashion with respect to designated reference points. We further define a pose confidence measure as the average probability of pixel back-projections in the Gaussian mixture. The goal of our experiments is two-fold. a) We demonstrate that our functional representation is sufficiently accurate as a shape template on which the probability of back-projected object points can be evaluated, and, b) we show that the resulting confidence scores based on these probabilities are indeed a consistent quality measure of pose.