Animated Point Cloud Motion Compression via Transform Encoding
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Solution Overview
Problem
Existing compression techniques for animations, such as HEVC and MPEG, are ineffective for compressing animated point clouds and other 3D formats, leading to large file sizes that are difficult to distribute and stream across data networks.
Innovation Solution
The development of systems and methods that compress motion in point clouds by analyzing the animated point cloud, detecting motion, and modeling it using transforms such as pivot points, joints, and connectors, which are then linked to sets of points to recreate the animation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing compression techniques (HEVC, MPEG) are applied to animated point clouds, then animation quality can be maintained, but file size remains large and distribution becomes difficult
Solution Approach 1:
The patent segments the animated point cloud into multiple subsets of points, where each subset is associated with a specific transform. This segmentation allows the system to compress different regions of the point cloud independently using appropriate transforms, reducing overall file size while maintaining animation quality for each segment.
Solution Approach 2:
The patent changes the representation parameters by replacing detailed positional and non-positional data with transform parameters (such as pivot points, joints, and connectors). This parameter transformation significantly reduces the data required to represent animated point clouds while enabling reconstruction of the original animation through the applied transforms.
2Measurement precision
If detailed positional data is stored for every frame of animated point cloud, then animation quality is high, but data volume increases significantly
Solution Approach 1:
Instead of storing complete positional data for every frame, the patent creates a compressed representation using transforms that can generate or copy the motion patterns. The transform parameters serve as a compact copy of the motion information, allowing reconstruction of detailed positional data only when needed for rendering, thereby reducing stored data volume while preserving animation quality.
3Quantity of substance
If transforms are applied to compress point cloud motion, then file size is reduced, but complexity of encoding increases
Solution Approach 1:
The patent employs dynamic transforms that can adapt to different regions and motions within the point cloud. The system selects and applies appropriate transforms (such as rigid body transforms, deformable transforms, or hybrid transforms) based on the characteristics of each point subset, enabling efficient compression while managing encoding complexity through adaptive transform selection.
Data Source
AI summary
Disclosed is a system and associated methods for compressing motion within an animated point cloud. The resulting compressed file encodes different transforms that recreate the motion of different sets of points across different point clouds or frames of the animation in place of the data for the different sets of points from the different point clouds. The compression involves detecting a motion that changes positioning of a set of points between a first point cloud and subsequent point clouds of an uncompressed encoding of two or more frames of an animation. The compression further involves defining a transform that models the motion, and generating a compressed animated point cloud by encoding the data of the first point cloud in the compressed animated point cloud, and by replacing the data for the set of points in the one or more subsequent point clouds with the transform.