Point clouds acquired through laser scanning and stereo vision techniques have been
applied in a wide range of applications, proving to be optimal sources for map** 3D urban …

Nowadays, and more than a decade after the first steps towards autonomous driving, we
keep heading to achieve fully autonomous vehicles on our roads, with LiDAR sensors being …

Due to the increased popularity of augmented and virtual reality experiences, the interest in
capturing the real world in multiple dimensions and in presenting it to users in an immersible …

Three-dimensional models provide a volumetric representation of space which is important
for a variety of robotic applications including flying robots and robots that are equipped with …

This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (aka,
Learned-PCGC) system, leveraging stacked Deep Neural Networks (DNN) based …

We present a generic and real-time time-varying point cloud codec for 3D immersive video.
This codec is suitable for mixed reality applications in which 3D point clouds are acquired at …

In point cloud compression, sufficient contexts are significant for modeling the point cloud
distribution. However, the contexts gathered by the previous voxel-based methods decrease …

We present a novel deep compression algorithm to reduce the memory footprint of LiDAR
point clouds. Our method exploits the sparsity and structural redundancy between points to …

In free-viewpoint video, there is a recent trend to represent scene objects as solids rather
than using multiple depth maps. Point clouds have been used in computer graphics for a …

In recent years, 3D point clouds have enjoyed a great popularity for representing both static
and dynamic 3D objects. When compared to 3D meshes, they offer the advantage of …