Point Cloud Encoding Control for Scalable Decoding Compatibility
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The mismatch between the reference structure of attribute data and the tree structure of geometry data during scalable decoding of point cloud data, resulting in the need for complex processing and potential failure of scalable decoding when geometry data is scaled.
Innovation Solution
Prohibit the combined use of scalable encoding and scaling encoding that involves a change in the tree structure of geometry data during point cloud encoding, ensuring that the reference structure of attribute data matches the geometry data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If geometry data is scaled to thin out points, then the number of points is reduced and processing efficiency is improved, but the tree structure of geometry data changes and causes mismatch with attribute data reference structure
Solution Approach 1:
The patent applies preliminary action by establishing the tree structure of geometry data before scaling operations. The reference structure of attribute data is configured to match the initial tree structure, and then scaling is performed while maintaining this pre-established structure. This ensures that even after point thinning, the structural relationship between geometry and attribute data remains consistent, preventing mismatch during scalable decoding.
2Reliability
If the reference structure of attribute data is changed to correspond to scaling of geometry data, then scalable decoding can be maintained, but encoding complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the encoding process into distinct phases: first establishing the tree structure and configuring the reference structure of attribute data, then performing scaling operations. This segmentation allows the reference structure to be fixed based on the initial tree structure, avoiding the need to dynamically change it during scaling. The result is maintained scalable decoding capability without requiring complex adaptive reconfiguration of the reference structure.
3Adaptability or versatility
If scalable encoding is used to generate scalably decodable coded data, then decoding flexibility is improved, but combined use with scaling encoding causes structure mismatch
Solution Approach 1:
The patent applies preliminary action by configuring the reference structure of attribute data to match the tree structure of geometry data before any scaling operations occur. This pre-configuration ensures that when scaling is subsequently applied, the structural relationship is already established and maintained. The result is that scalable encoding and scaling encoding can be combined without causing structure mismatch, as the reference structure was set to correspond to the initial tree structure that persists through scaling.
Data Source
AI summary
There is provided an information processing apparatus and method that enable to more easily achieve scalable decoding of point cloud data. In encoding of a point cloud representing an object having a three-dimensional shape as a set of points, control is performed so as to prohibit combined use of scalable encoding that is an encoding method for generating scalably decodable coded data and scaling encoding that is an encoding method involving a change in a tree structure of geometry data. The present disclosure can be applied to, for example, an information processing apparatus, an image processing apparatus, an encoding device, a decoding device, an electronic device, an information processing method, a program, or the like.