Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Point cloud completion is a generation and estimation issue derived from the partial point
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …

Point clouds captured in real-world applications are often incomplete due to the limited
sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point …

Large-scale labeled data are generally required to train deep neural networks in order to
obtain better performance in visual feature learning from images or videos for computer …

As 3D point clouds become the representation of choice for multiple vision and graphics
applications, the ability to synthesize or reconstruct high-resolution, high-fidelity point clouds …

Shape completion, the problem of estimating the complete geometry of objects from partial
observations, lies at the core of many vision and robotics applications. In this work, we …

Estimating the complete 3D point cloud from an incomplete one is a key problem in many
vision and robotics applications. Mainstream methods (eg, PCN and TopNet) use Multi-layer …

Recent deep networks that directly handle points in a point set, eg, PointNet, have been
state-of-the-art for supervised learning tasks on point clouds such as classification and …

Abstract 3D point cloud generation is of great use for 3D scene modeling and
understanding. Real-world 3D object point clouds can be properly described by a collection …

Three-dimensional geometric data offer an excellent domain for studying representation
learning and generative modeling. In this paper, we look at geometric data represented as …