Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation

• URL: http://arxiv.org/abs/2003.14166v2
• Date: Fri, 17 Apr 2020 13:22:58 GMT
• Title: Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation
• Authors: Sai Rajeswar, Fahim Mannan, Florian Golemo, J\'er\^ome Parent-L\'evesque, David Vazquez, Derek Nowrouzezahrai, Aaron Courville
• Abstract summary: Pix2Shape generates 3D scenes from a single input image without supervision. We show that Pix2Shape learns a consistent scene representation in its encoded latent space. We evaluate Pix2Shape with experiments on the ShapeNet dataset.
• Score: 20.788952043643906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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