  This paper presents a method to reconstruct the 3D structure of generic
convex rooms from sound signals. Differently from most of the previous
approaches, the method is fully uncalibrated in the sense that no knowledge
about the microphones and sources position is needed. Moreover, we demonstrate
that it is possible to bypass the well known echo labeling problem, allowing to
reconstruct the room shape in a reasonable computation time without the need of
additional hypotheses on the echoes order of arrival. Finally, the method is
intrinsically robust to outliers and missing data in the echoes detection,
allowing to work also in low SNR conditions. The proposed pipeline formalises
the problem in different steps such as time of arrival estimation, microphones
and sources localization and walls estimation. After providing a solution to
these different problems we present a global optimization approach that links
together all the problems in a single optimization function. The accuracy and
robustness of the method is assessed on a wide set of simulated setups and in a
challenging real scenario. Moreover we make freely available for a challenging
dataset for 3D room reconstruction with accurate ground truth in a real
scenario.
