  Reconstructing high-fidelity 3D objects from sparse, partial observation 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 datasets, they perform poorly on real-world data
that is sparse and noisy. This paper analyzes the root cause of such deficient
performance of a popular neural implicit model. We discover that the
limitations are due to highly complicated objectives, lack of regularization,
and poor initialization. To overcome these issues, we introduce two simple yet
effective modifications: (i) a deep encoder that provides a better and more
stable initialization for latent code optimization; and (ii) a deep
discriminator that serves as a prior model to boost the fidelity of the shape.
We evaluate our approach on two real-wold self-driving datasets and show
superior performance over state-of-the-art 3D object reconstruction methods.
