  In this paper, we develop a new method, termed SDF-3DGAN, for 3D object
generation and 3D-Aware image synthesis tasks, which introduce implicit Signed
Distance Function (SDF) as the 3D object representation method in the
generative field. We apply SDF for higher quality representation of 3D object
in space and design a new SDF neural renderer, which has higher efficiency and
higher accuracy. To train only on 2D images, we first generate the objects,
which are represented by SDF, from Gaussian distribution. Then we render them
to 2D images and use them to apply GAN training method together with 2D images
in the dataset. In the new rendering method, we relieve all the potential of
SDF mathematical property to alleviate computation pressure in the previous SDF
neural renderer. In specific, our new SDF neural renderer can solve the problem
of sampling ambiguity when the number of sampling point is not enough, \ie use
the less points to finish higher quality sampling task in the rendering
pipeline. And in this rendering pipeline, we can locate the surface easily.
Therefore, we apply normal loss on it to control the smoothness of generated
object surface, which can make our method enjoy the much higher generation
quality. Quantitative and qualitative experiments conducted on public
benchmarks demonstrate favorable performance against the state-of-the-art
methods in 3D object generation task and 3D-Aware image synthesis task. Our
codes will be released at https://github.com/lutao2021/SDF-3DGAN.

Computer Vision and Pattern Recognition