  Deep implicit field regression methods are effective for 3D reconstruction
from single-view images. However, the impact of different sampling patterns on
the reconstruction quality is not well-understood. In this work, we first study
the effect of point set discrepancy on the network training. Based on Farthest
Point Sampling algorithm, we propose a sampling scheme that theoretically
encourages better generalization performance, and results in fast convergence
for SGD-based optimization algorithms. Secondly, based on the reflective
symmetry of an object, we propose a feature fusion method that alleviates
issues due to self-occlusions which makes it difficult to utilize local image
features. Our proposed system Ladybird is able to create high quality 3D object
reconstructions from a single input image. We evaluate Ladybird on a large
scale 3D dataset (ShapeNet) demonstrating highly competitive results in terms
of Chamfer distance, Earth Mover's distance and Intersection Over Union (IoU).
