  Reconstructing high-quality 3D objects from sparse, partial observations from
a single view is of crucial importance for various applications in computer
vision, robotics, and graphics. While recent neural implicit modeling methods
show promising results on synthetic or dense data, they perform poorly on
sparse and noisy real-world data. We discover that the limitations of a popular
neural implicit model are due to lack of robust shape priors and lack of proper
regularization. In this work, we demonstrate highquality in-the-wild shape
reconstruction using: (i) a deep encoder as a robust-initializer of the shape
latent-code; (ii) regularized test-time optimization of the latent-code; (iii)
a deep discriminator as a learned high-dimensional shape prior; (iv) a novel
curriculum learning strategy that allows the model to learn shape priors on
synthetic data and smoothly transfer them to sparse real world data. Our
approach better captures the global structure, performs well on occluded and
sparse observations, and registers well with the ground-truth shape. We
demonstrate superior performance over state-of-the-art 3D object reconstruction
methods on two real-world datasets.
