  We present a novel neural implicit shape method for partial point cloud
completion. To that end, we combine a conditional Deep-SDF architecture with
learned, adversarial shape priors. More specifically, our network converts
partial inputs into a global latent code and then recovers the full geometry
via an implicit, signed distance generator. Additionally, we train a PointNet++
discriminator that impels the generator to produce plausible, globally
consistent reconstructions. In that way, we effectively decouple the challenges
of predicting shapes that are both realistic, i.e. imitate the training set's
pose distribution, and accurate in the sense that they replicate the partial
input observations. In our experiments, we demonstrate state-of-the-art
performance for completing partial shapes, considering both man-made objects
(e.g. airplanes, chairs, ...) and deformable shape categories (human bodies).
Finally, we show that our adversarial training approach leads to visually
plausible reconstructions that are highly consistent in recovering missing
parts of a given object.
