  In this paper, we address the problem of estimating the in-hand 6D pose of an
object in contact with multiple vision-based tactile sensors. We reason on the
possible spatial configurations of the sensors along the object surface.
Specifically, we filter contact hypotheses using geometric reasoning and a
Convolutional Neural Network (CNN), trained on simulated object-agnostic
images, to promote those that better comply with the actual tactile images from
the sensors. We use the selected sensors configurations to optimize over the
space of 6D poses using a Gradient Descent-based approach. We finally rank the
obtained poses by penalizing those that are in collision with the sensors. We
carry out experiments in simulation using the DIGIT vision-based sensor with
several objects, from the standard YCB model set. The results demonstrate that
our approach estimates object poses that are compatible with actual
object-sensor contacts in $87.5\%$ of cases while reaching an average
positional error in the order of $2$ centimeters. Our analysis also includes
qualitative results of experiments with a real DIGIT sensor.
