Autoregressive and Residual Index Convolution Model for Point Cloud Geometry Compression.

Area of Science:

• Computer Vision
• Data Compression
• Machine Learning

Background:

• Existing point cloud compression methods struggle to achieve state-of-the-art performance due to a lack of autoregressive approaches.
• Previous entropy compression models showed promise but required further optimization for competitive results.

Purpose of the Study:

• To introduce a hybrid point cloud compression method incorporating an autoregressive strategy.
• To enhance compression efficiency and performance by improving voxel occupancy estimation.
• To establish a new lower-bound bitrate for point cloud data compression.

Main Methods:

• A hybrid approach combining octree-nodes and voxel occupancy estimation.
• Implementation of a Binary Arithmetic Range Coder for bitrate calculation.
• Adaptation of an autoregressive grouping method for iterative voxel candidate estimation.
• Refactoring the backbone architecture with distiller layers and lightweight 1D convolutions.
• Optimization of cross-entropy to analyze causal relationships and replace traditional convolution techniques.

Main Results:

• The proposed model demonstrates significant improvements in both time and memory consumption.
• Achieved superior compression performance compared to recent deep learning-based methods on multiple datasets.
• Successfully replaced computationally expensive voxel convolution and attention mechanisms.

Conclusions:

• The developed autoregressive point cloud compression method offers state-of-the-art performance.
• The hybrid approach provides a more efficient and effective solution for point cloud data compression.
• This work sets a new benchmark for deep learning-based point cloud compression.