  We infer and generate three-dimensional (3D) scene information from a single
input image and without supervision. This problem is under-explored, with most
prior work relying on supervision from, e.g., 3D ground-truth, multiple images
of a scene, image silhouettes or key-points. We propose Pix2Shape, an approach
to solve this problem with four components: (i) an encoder that infers the
latent 3D representation from an image, (ii) a decoder that generates an
explicit 2.5D surfel-based reconstruction of a scene from the latent code (iii)
a differentiable renderer that synthesizes a 2D image from the surfel
representation, and (iv) a critic network trained to discriminate between
images generated by the decoder-renderer and those from a training
distribution. Pix2Shape can generate complex 3D scenes that scale with the
view-dependent on-screen resolution, unlike representations that capture
world-space resolution, i.e., voxels or meshes. We show that Pix2Shape learns a
consistent scene representation in its encoded latent space and that the
decoder can then be applied to this latent representation in order to
synthesize the scene from a novel viewpoint. We evaluate Pix2Shape with
experiments on the ShapeNet dataset as well as on a novel benchmark we
developed, called 3D-IQTT, to evaluate models based on their ability to enable
3d spatial reasoning. Qualitative and quantitative evaluation demonstrate
Pix2Shape's ability to solve scene reconstruction, generation, and
understanding tasks.
