One of the main challenges in machine vision relates to the problem of obtaining robust
representation of visual features that remain unaffected by geometric transformations. This …

In this paper, we introduce Point2Mesh, a technique for reconstructing a surface mesh from
an input point cloud. Instead of explicitly specifying a prior that encodes the expected shape …

Intelligent Mesh Generation (IMG) represents a novel and promising field of research,
utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG …

The success of the transformer architecture in natural language processing has recently
triggered attention in the computer vision field. The transformer has been used as a …

We introduce TM-NET, a novel deep generative model for synthesizing textured meshes in a
part-aware manner. Once trained, the network can generate novel textured meshes from …

Online 3D semantic segmentation, which aims to perform real-time 3D scene reconstruction
along with semantic segmentation, is an important but challenging topic. A key challenge is …

Geometric deep learning has sparked a rising interest in computer graphics to perform
shape understanding tasks, such as shape classification and semantic segmentation. When …

In this work, we tackle the problem of single image-based 3D shape retrieval (IBSR), where
we seek to find the most matched shape of a given single 2D image from a shape repository …

Convolutionalneural networks (CNNs) have made great breakthroughs in two-dimensional
(2D) computer vision. However, their irregular structure makes it hard to harness the …

Deep learning-based approaches for shape understanding and processing tasks have
attracted considerable attention. Despite the great progress that has been made, the existing …