Abstract Three-dimensional (3D) object recognition is widely used in automated driving,
medical image analysis, virtual/augmented reality, artificial intelligence robots, and other …

Representing shapes as level sets of neural networks has been recently proved to be useful
for different shape analysis and reconstruction tasks. So far, such representations were …

Deep learning approach has been used extensively in image analysis tasks. However,
implementing the methods in 3D data is a bit complex because most of the previously …

Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in
machine perception. To address this problem we introduce Deep Local Shapes (DeepLS), a …

Computer graphics, 3D computer vision and robotics communities have produced multiple
approaches to representing 3D geometry for rendering and reconstruction. These provide …

We advocate the use of implicit fields for learning generative models of shapes and
introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving …

Recently, neural networks have been used as implicit representations for surface
reconstruction, modelling, learning, and generation. So far, training neural networks to be …

Point clouds provide a flexible geometric representation suitable for countless applications
in computer graphics; they also comprise the raw output of most 3D data acquisition devices …

Polygonal meshes provide an efficient representation for 3D shapes. They explicitly
captureboth shape surface and topology, and leverage non-uniformity to represent large flat …

We introduce a method for learning to generate the surface of 3D shapes. Our approach
represents a 3D shape as a collection of parametric surface elements and, in contrast to …