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

In this paper, we propose a two-stage deep learning framework called VoxelContext-Net for
both static and dynamic point cloud compression. Taking advantages of both octree based …

Recent years have witnessed the growth of point cloud based applications for both
immersive media as well as 3D sensing for auto-driving, because of its realistic and fine …

In this paper, we propose a novel local descriptor-based framework, called You Only
Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to …

We present RoReg, a novel point cloud registration framework that fully exploits oriented
descriptors and estimated local rotations in the whole registration pipeline. Previous …

This study develops a unified Point Cloud Geometry (PCG) compression method through the
processing of multiscale sparse tensor-based voxelized PCG. We call this compression …

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 …

Local density of point clouds is crucial for representing local details, but has been
overlooked by existing point cloud compression methods. To address this, we propose a …

Motivated by the intuition that one can transform two aligned point clouds to each other more
easily and meaningfully than a misaligned pair, we propose CorrNet3D-the first …

Many modern robotics applications rely on 3D maps of the environment. Due to the large
memory requirements of dense 3D maps, compression techniques are often necessary to …