  3D object reconstruction is a fundamental task of many robotics and AI
problems. With the aid of deep convolutional neural networks (CNNs), 3D object
reconstruction has witnessed a significant progress in recent years. However,
possibly due to the prohibitively high dimension of the 3D object space, the
results from deep CNNs are often prone to missing some shape details. In this
paper, we present an approach which aims to preserve more shape details and
improve the reconstruction quality. The key idea of our method is to leverage
object mask and pose estimation from CNNs to assist the 3D shape learning by
constructing a probabilistic single-view visual hull inside of the network. Our
method works by first predicting a coarse shape as well as the object pose and
silhouette using CNNs, followed by a novel 3D refinement CNN which refines the
coarse shapes using the constructed probabilistic visual hulls. Experiment on
both synthetic data and real images show that embedding a single-view visual
hull for shape refinement can significantly improve the reconstruction quality
by recovering more shapes details and improving shape consistency with the
input image.
