Semantic segmentation in 3D meshes is the classification of its constituent element (s) into
specific classes or categories. Using the powerful feature extraction abilities of deep neural …

Motivated by the vast success of deep convolutional networks, there is a great interest in
generalizing convolutions to non-Euclidean manifolds. A major complication in comparison …

Robot manipulation, for example, pick-and-place manipulation, is broadly used for intelligent
manufacturing with industrial robots, ocean engineering with underwater robots, service …

Surface meshes are widely used shape representations and capture finer geometry data
than point clouds or volumetric grids, but are challenging to apply CNNs directly due to their …

3D shape representation and its processing have substantial effects on 3D shape
recognition. The polygon mesh as a 3D shape representation has many advantages in …

Point cloud analysis has a wide range of applications in many areas such as computer
vision, robotic manipulation, and autonomous driving. While deep learning has achieved …

Three-dimensional (3-D) object recognition has achieved satisfied achievement in both
academia and industry. However, most traditional 3-D object classification methods implicitly …

Variational autoencoders (VAEs) play an important role in high-dimensional data generation
based on their ability to fuse the stochastic data representation with the power of recent …

The Markov Random Field (MRF) energy function, constructed by existing OpenMVS-based
3D texture reconstruction algorithms, considers only the image label of the adjacent triangle …

The increased development of camera resolution, processing power, and aerial platforms
helped to create more cost-efficient approaches to capture and generate point clouds to …