High-quality 3D reconstruction is an important topic in computer graphics and computer
vision with many applications, such as robotics and augmented reality. The advent of …

We introduce a new general-purpose approach to deep learning on three-dimensional
surfaces based on the insight that a simple diffusion layer is highly effective for spatial …

Template 3D shapes are useful for many tasks in graphics and vision, including fitting
observation data, analyzing shape collections, and transferring shape attributes. Because of …

While implicit representations have achieved high-fidelity results in 3D rendering, it remains
challenging to deforming and animating the implicit field. Existing works typically leverage …

This paper introduces a deep architecture for segmenting 3D objects into their labeled
semantic parts. Our architecture combines image-based Fully Convolutional Networks …

Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required
to be consolidated. In this paper, we present the first deep learning based {em edge-aware} …

In this paper, we present a novel approach for 3D dental model segmentation via deep
Convolutional Neural Networks (CNNs). Traditional geometry-based methods tend to …

Parametric modeling of 3D body shape is widely used to create realistic human bodies. It
furthermore permits robust reconstruction of complete 3D body shapes even from …

The amount of captured 3D data is continuously increasing, with the democratization of
consumer depth cameras, the development of modern multi‐view stereo capture setups and …

We present a novel rotation invariant architecture operating directly on point cloud data. We
demonstrate how rotation invariance can be injected into a recently proposed point-based …