Z Que, G Lu, D Xu - … of the IEEE/CVF Conference on …, 2021 - openaccess.thecvf.com
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 …
J Wang, D Ding, Z Li, Z Ma - 2021 Data Compression …, 2021 - ieeexplore.ieee.org
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 …
H Wang, Y Liu, Q Hu, B Wang, J Chen… - … on Pattern Analysis …, 2023 - ieeexplore.ieee.org
We present RoReg, a novel point cloud registration framework that fully exploits oriented descriptors and estimated local rotations in the whole registration pipeline. Previous …
J Wang, D Ding, Z Li, X Feng, C Cao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …
L Huang, S Wang, K Wong, J Liu… - Proceedings of the …, 2020 - openaccess.thecvf.com
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 …