  Recently, learning-based approaches for 3D reconstruction from 2D images have
gained popularity due to its modern applications, e.g., 3D printers, autonomous
robots, self-driving cars, virtual reality, and augmented reality. The computer
vision community has applied a great effort in developing functions to
reconstruct the full 3D geometry of objects and scenes. However, to extract
image features, they rely on convolutional neural networks, which are
ineffective in capturing long-range dependencies. In this paper, we propose to
substantially improve Occupancy Networks, a state-of-the-art method for 3D
object reconstruction. For such we apply the concept of self-attention within
the network's encoder in order to leverage complementary input features rather
than those based on local regions, helping the encoder to extract global
information. With our approach, we were capable of improving the original work
in 5.05% of mesh IoU, 0.83% of Normal Consistency, and more than 10X the
Chamfer-L1 distance. We also perform a qualitative study that shows that our
approach was able to generate much more consistent meshes, confirming its
increased generalization power over the current state-of-the-art.
