Abstract. In this paper, we present a framework for reconstructing a
point-based 3D model of an object from a single-view image. We found
distance metrics, like Chamfer distance, were used in previous work to
measure the difference of two point sets and serve as the loss function
in point-based reconstruction. However, such point-point loss does not
constrain the 3D model from a global perspective. We propose adding
geometric adversarial loss (GAL). It is composed of two terms where
the geometric loss ensures consistent shape of reconstructed 3D models
close to ground-truth from different viewpoints, and the conditional
adversarial loss generates a semantically-meaningful point cloud. GAL
benefits predicting the obscured part of objects and maintaining geometric
structure of the predicted 3D model. Both the qualitative results and
quantitative analysis manifest the generality and suitability of our method