Point Cloud Completion: A Survey.

Point cloud completion is the task of producing a complete 3D shape given an input of a partial point cloud. It has become a vital process in 3D computer graphics, vision and applications such as autonomous driving, robotics, and augmented reality. These applications often rely on the presence of a complete 3D representation of the environment.

An Analysis of Machine- and Human-Analytics in Classification.

In this work, we present a study that traces the technical and cognitive processes in two visual analytics applications to a common theoretic model of soft knowledge that may be added into a visual analytics process for constructing a decision-tree model. Both case studies involved the development of classification models based on the "bag of features" approach. Both compared a visual analytics approach using parallel coordinates with a machine-learning approach using information theory.

Registration of 3D point clouds and meshes: a survey from rigid to nonrigid.

Three-dimensional surface registration transforms multiple three-dimensional data sets into the same coordinate system so as to align overlapping components of these sets. Recent surveys have covered different aspects of either rigid or nonrigid registration, but seldom discuss them as a whole. Our study serves two purposes: 1) To give a comprehensive survey of both types of registration, focusing on three-dimensional point clouds and meshes and 2) to provide a better understanding of registration from the perspective of data fitting.

Embedding retrieval of articulated geometry models.

Due to the popularity of computer games and animation, research on 3D articulated geometry model retrieval has attracted a lot of attention in recent years. However, most existing works extract high-dimensional features to represent models and suffer from practical limitations. First, misalignment in high-dimensional features may produce unreliable euclidean distances and affect retrieval accuracy.

Deformable model retrieval based on topological and geometric signatures.

With the increasing popularity of 3D applications such as computer games, a lot of 3D geometry models are being created. To encourage sharing and reuse, techniques that support matching and retrieval of these models are emerging. However, only a few of them can handle deformable models, that is, models of different poses, and these methods are generally very slow.