Laplacian matrix learning for point cloud attribute compression with ternary search-based adaptive block partition
C Peng, W Gao - Proceedings of the 32nd ACM International …, 2024 - dl.acm.org
Graph Fourier Transform (GFT) has demonstrated significant effectiveness in point cloud
attribute compression task. However, existing graph modeling methods are based on the …
attribute compression task. However, existing graph modeling methods are based on the …
Dcnet: Large-scale point cloud semantic segmentation with discriminative and efficient feature aggregation
The point cloud feature aggregation, which learns discriminative features from the
disordered points, plays a key role for large-scale point cloud semantic segmentation. Most …
disordered points, plays a key role for large-scale point cloud semantic segmentation. Most …
Real-time scene-aware LiDAR point cloud compression using semantic prior representation
Existing LiDAR point cloud compression (PCC) methods tend to treat compression as a
fidelity issue, without sufficiently addressing its machine perception aspect. The latter issue …
fidelity issue, without sufficiently addressing its machine perception aspect. The latter issue …
Block-adaptive point cloud attribute coding with region-aware optimized transform
Block-based compression scheme shows remarkable success in image and video coding.
However, existing tree-type block partition methods usually divide point clouds into clusters …
However, existing tree-type block partition methods usually divide point clouds into clusters …
Non-rigid registration-based progressive motion compensation for point cloud geometry compression
There is a critical requirement for efficiently compressing point cloud geometries
representing 3-D moving objects in various applications. The Moving Picture Experts Group …
representing 3-D moving objects in various applications. The Moving Picture Experts Group …
Improved video-based point cloud compression via segmentation
A point cloud is a representation of objects or scenes utilising unordered points comprising
3D positions and attributes. The ability of point clouds to mimic natural forms has gained …
3D positions and attributes. The ability of point clouds to mimic natural forms has gained …
PDE-based Progressive Prediction Framework for Attribute Compression of 3D Point Clouds
X Yang, Y Shao, S Liu, TH Li, G Li - Proceedings of the 31st ACM …, 2023 - dl.acm.org
In recent years, the diffusion-based image compression scheme has achieved significant
success, which inspires us to use diffusion theory to employ the diffusion model for point …
success, which inspires us to use diffusion theory to employ the diffusion model for point …
Real-time LiDAR point cloud compression using bi-directional prediction and range-adaptive floating-point coding
Due to the large amount of data involved in the three-dimensional (3D) LiDAR point clouds,
point cloud compression (PCC) becomes indispensable to many real-time applications. In …
point cloud compression (PCC) becomes indispensable to many real-time applications. In …
Isolated points prediction via deep neural network on point cloud lossless geometry compression
Z Wei, B Niu, H **ao, Y He - … on Circuits and Systems for Video …, 2022 - ieeexplore.ieee.org
The point cloud is one of the indispensable data structures of virtual and mixed reality
applications. Vivid scene requirement means that millions of points need to be encoded …
applications. Vivid scene requirement means that millions of points need to be encoded …
Local geometry-based intra prediction for octree-structured geometry coding of point clouds
Z Wang, S Wan, L Wei - … on Circuits and Systems for Video …, 2022 - ieeexplore.ieee.org
Point cloud compression (PCC) is crucial for efficient and flexible storage as well as feasible
transmission of point clouds in practice. For geometry compression, one popular approach is …
transmission of point clouds in practice. For geometry compression, one popular approach is …