Binary Voxel Octree State Summarization
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Solution Overview
Problem
Current point cloud compression methods are inefficient for large-scale point cloud data processing and transmission, particularly in applications like autonomous driving and virtual reality, due to high computational costs and storage requirements, and existing methods do not effectively handle dynamic point clouds with real-time processing needs.
Innovation Solution
The proposed method employs a binary voxel-based octree coding scheme that uses context modeling and state summarization to predict occupancy probabilities for voxels, enabling efficient encoding and decoding of point cloud data through adaptive arithmetic coding, which reduces the computational burden and improves compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional point cloud compression methods are used, then compression is achieved, but computational cost and storage requirements become excessively high for large-scale data
Solution Approach 1:
The point cloud data is segmented into multiple octants using an octree structure, where each octant represents a subdivided spatial region. This segmentation allows independent processing and compression of different spatial regions, reducing overall computational complexity while maintaining compression effectiveness for large-scale point cloud data
Solution Approach 2:
The patent transitions from processing individual 3D points to processing volumetric voxels in a 3D grid space. By organizing points into voxel grids and using octree-based hierarchical decomposition, the method adds a spatial dimensionality layer that enables more efficient compression representation and reduces storage requirements while managing computational complexity
2Productivity
If existing compression methods are applied to dynamic point clouds, then compression is achieved, but real-time processing capabilities are insufficient
Solution Approach 1:
The patent performs preliminary organization of point cloud data into octree structures and voxel grids before compression. By pre-establishing the hierarchical spatial framework and identifying occupied versus empty voxels in advance, the method enables faster real-time processing of dynamic point clouds without sacrificing compression efficiency
Solution Approach 2:
The compression method is designed to handle dynamic point clouds by allowing incremental updates to the octree structure as new points are added or existing points move. The context modeling adapts dynamically to changing occupancy patterns, enabling real-time processing capability while maintaining compression performance
3Quantity of substance
If high compression efficiency is achieved through detailed context modeling, then compression ratio improves, but computational burden increases
Solution Approach 1:
The patent applies context modeling selectively based on local occupancy patterns. Instead of uniformly applying complex context modeling to all voxels, the method identifies regions with different occupancy characteristics and applies appropriate modeling strategies locally, improving compression efficiency for occupied regions while reducing computational burden for empty or uniformly structured regions
Solution Approach 2:
The method dynamically adjusts context modeling parameters based on the occupancy state of neighboring voxels. By changing the complexity of context modeling according to local data characteristics (e.g., using simpler models for homogeneous regions and more complex models for heterogeneous regions), the patent achieves high compression efficiency while managing computational burden
Data Source
AI summary
In one implementation, we improve the binary voxel-based octree coding method, via a proposed state summarization module for context modeling. Given a current voxel to be encoded or decoded, instead of directly estimating its occupancy probability based on the associated binary occupancy context, a proposed state summarization module is applied to convert the original binary context to a summarized representation. Under the summarized representation, the estimation of the occupancy probability becomes more affordable and effective. In particular, density-based state summarization, pattern-based, learning-based state summarization, and learning-based state summarization methods are provided.