  Recently, several works have addressed modeling of 3D shapes using deep
neural networks to learn implicit surface representations. Up to now, the
majority of works have concentrated on reconstruction quality, paying little or
no attention to model size or training time. This work proposes LightSAL, a
novel deep convolutional architecture for learning 3D shapes; the proposed work
concentrates on efficiency both in network training time and resulting model
size. We build on the recent concept of Sign Agnostic Learning for training the
proposed network, relying on signed distance fields, with unsigned distance as
ground truth. In the experimental section of the paper, we demonstrate that the
proposed architecture outperforms previous work in model size and number of
required training iterations, while achieving equivalent accuracy. Experiments
are based on the D-Faust dataset that contains 41k 3D scans of human shapes.
The proposed model has been implemented in PyTorch.
