  Neural implicit surface representation methods have recently shown impressive
3D reconstruction results. However, existing solutions struggle to reconstruct
urban outdoor scenes due to their large, unbounded, and highly detailed nature.
Hence, to achieve accurate reconstructions, additional supervision data such as
LiDAR, strong geometric priors, and long training times are required. To tackle
such issues, we present SCILLA, a new hybrid implicit surface learning method
to reconstruct large driving scenes from 2D images. SCILLA's hybrid
architecture models two separate implicit fields: one for the volumetric
density and another for the signed distance to the surface. To accurately
represent urban outdoor scenarios, we introduce a novel volume-rendering
strategy that relies on self-supervised probabilistic density estimation to
sample points near the surface and transition progressively from volumetric to
surface representation. Our solution permits a proper and fast initialization
of the signed distance field without relying on any geometric prior on the
scene, compared to concurrent methods. By conducting extensive experiments on
four outdoor driving datasets, we show that SCILLA can learn an accurate and
detailed 3D surface scene representation in various urban scenarios while being
two times faster to train compared to previous state-of-the-art solutions.
