Learning Shape Priors for Single-View 3D Completion and Reconstruction
Current methods for 3D objects reconstruction often generate unnatural objects. The authors propose to train the network to only generates natural objects.
The task is the following, from a 2D image, the model generates the full 3D object. The output is a 3D cube and is learned by binary crossentropy.
The metrics used are the IoU between the volume generated and the groundtruth volume or the Chamfer Distance, which is the distance between a point and its closest groundtruth point. On most task, they are even better than methods that require a mask and are overall better than all previous methods.
To enforce the naturalness of the object, they train a WGAN that will classify whether an object is natural or not.