Point Cloud Encoding Adaptive Grouping Spatial Consistency
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Solution Overview
Problem
Existing point cloud encoding and decoding processes fail to effectively ensure spatial consistency of patches across consecutive frames, leading to degraded video coding and patch auxiliary information coding performance due to the lack of consideration for correlation between frames.
Innovation Solution
An adaptive grouping method is employed to divide point cloud frames into subgroups based on characteristic information, such as occupancy map size, and encode this subgroup information into a bitstream using fixed or variable-length encoding schemes, ensuring accurate decoding and improved coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If point cloud frames are processed based on fixed frame length, then the encoding process is simple, but spatial consistency of patches across consecutive frames cannot be effectively ensured
Solution Approach 1:
The patent divides the sequence of N point cloud frames into M subgroups, where each subgroup contains a variable number of frames. This segmentation allows the encoder to process frames in meaningful units rather than fixed lengths, enabling consideration of temporal correlation between frames while maintaining manageable encoding complexity through structured division.
Solution Approach 2:
The patent employs adaptive grouping where the number of frames in each subgroup and the timing of subgroup boundaries are determined dynamically based on the actual point cloud data characteristics. This dynamic approach allows the encoder to adjust the grouping strategy to optimize both spatial consistency and encoding efficiency without rigid fixed-frame constraints.
2Device complexity
If correlation between consecutive frames is not considered, then the encoding process is simpler, but video coding performance and patch auxiliary information coding performance are degraded
Solution Approach 1:
The patent merges the encoding of multiple consecutive frames into subgroups, allowing the encoder to process and represent temporal correlations between frames within each subgroup. This combining approach enables the encoder to exploit inter-frame dependencies for improved compression performance while maintaining manageable complexity through the structured subgroup organization.
Solution Approach 2:
The patent performs preliminary grouping of frames into subgroups before the actual encoding process. This preliminary organization prepares the data structure in advance, enabling the encoder to systematically consider temporal correlations and optimize coding parameters for each subgroup, thereby improving overall coding performance without adding complexity during the encoding operation itself.
3Reliability
If adaptive grouping is used to divide frames into subgroups, then spatial consistency of patches is improved, but the complexity of determining subgroups increases
Solution Approach 1:
The patent utilizes characteristic information of point cloud frames, such as occupancy map size, as parameters to guide the adaptive grouping process. By changing the grouping strategy based on these measurable parameters, the system can automatically determine optimal subgroups that ensure spatial consistency without requiring complex manual configuration or excessive computational analysis.
Data Source
AI summary
The point cloud encoding method includes obtaining subgroup information of N frames of point clouds, where the subgroup information includes a quantity M of subgroups into which the N frames of point clouds are divided or a quantity of frames of point clouds included in each of one or more subgroups among the M subgroups, and writing the subgroup information of the N frames of point clouds into a bitstream. The point cloud decoding method includes receiving a bitstream, parsing the bitstream to obtain subgroup information, where the subgroup information includes a quantity M of subgroups into which N frames of point clouds are divided or a quantity of frames of point clouds included in each of one or more subgroups among the M subgroups, and decoding the N frames of point clouds based on the subgroup information.