Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

Real-time rendering and animation of humans is a core function in games, movies, and
telepresence applications. Existing methods have a number of drawbacks we aim to …

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 …

As the basic task of point cloud analysis, classification is fundamental but always
challenging. To address some unsolved problems of existing methods, we propose a …

Abstract Knowledge transfer from synthetic to real data has been widely studied to mitigate
data annotation constraints in various computer vision tasks such as semantic segmentation …

In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point
Clouds with Deep Learning (DL) techniques can help to recognize historical architectural …

We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for
point clouds. Gradients for point locations and normals are carefully designed to handle …

3D data is a valuable asset the computer vision filed as it provides rich information about the
full geometry of sensed objects and scenes. Recently, with the availability of both large 3D …

Abstract Convolutional Neural Networks (CNNs) have performed extremely well on data
represented by regularly arranged grids such as images. However, directly leveraging the …

We propose Pulsar, an efficient sphere-based differentiable rendering module that is orders
of magnitude faster than competing techniques, modular, and easy-to-use due to its tight …