Deep Single-View 3D Object Reconstruction with Visual Hull Embedding

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.Comment: 11 page

Some majorization inequalities in multivariate analysis and their applications

Final report has title: Some majorization inequalities in multivariate analysis and their applicationsIssued as Progress reports [nos. 1-2], and Final report, Project no. G-37-63

Inequalities in multivariate analysis and reliability theory

Issued as Progress report, and Final report, Project no. G-37-63