Point clouds are becoming essential in key applications with advances in capture
technologies leading to large volumes of data. Compression is thus essential for storage …

Point cloud based 3D visual representation is becoming popular due to its ability to exhibit
the real world in a more comprehensive and immersive way. However, under a limited …

This paper addresses the problem of compression of 3D point cloud sequences that are
characterized by moving 3D positions and color attributes. As temporally successive point …

The state-of-the-art 2D-based dynamic point cloud (DPC) compression algorithm is the
video-based point cloud compression (V-PCC) developed by the Moving Pictures Experts …

Due to the enormous volume of point cloud data, transmitting and storing the data requires
large bandwidth and storage space. It could be a critical bottleneck, especially in tasks such …

Widely adopted motion forecasting datasets sub-stitute the observed sensory inputs with
higher-level abstractions such as 3D boxes and polylines. These sparse shapes are inferred …

3-D point clouds facilitate 3-D visual applications with detailed information of objects and
scenes but bring about enormous challenges to design efficient compression technologies …

Lidars are depth measuring sensors widely used in autonomous driving and augmented
reality. However, the large volume of data produced by lidars can lead to high costs in data …

We present a novel octree-based multi-level framework for large-scale point cloud
compression, which can organize sparse and unstructured point clouds in a memory …

In autonomous vehicles or robots, point clouds from LiDAR can provide accurate depth
information of objects compared with 2D images, but they also suffer a large volume of data …