Point Cloud Data Encoding via 2D Projection Planes
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Solution Overview
Problem
Traditional video coding techniques are inefficient for representing visual information of three-dimensional scenes, as they primarily work with two-dimensional video frames.
Innovation Solution
The method involves determining a 3D region of point cloud data and a corresponding 2D region of a point cloud track patch frame, and then reconstructing the 3D region using patch frame data and video frame data from point cloud component tracks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional two-dimensional video coding techniques are used, then the encoding process is simple, but the representation efficiency of three-dimensional visual scene information is poor
Solution Approach 1:
The patent transitions from traditional 2D video frame encoding to 3D point cloud data encoding by introducing spatial dimensionality. The system divides the 3D space into multiple 2D projection planes (front, back, left, right, top, bottom views) and encodes point cloud data from multiple angles simultaneously, thereby achieving efficient representation of three-dimensional visual information while maintaining manageable encoding complexity through structured dimensional decomposition.
2Loss of information
If point cloud data is encoded using multiple 2D projection planes, then the representation of 3D visual information improves, but the data processing complexity increases
Solution Approach 1:
The patent segments the 3D point cloud data into multiple 2D projection planes (front, back, left, right, top, bottom views), where each plane contains a subset of the point cloud data. This segmentation allows the complex 3D encoding task to be divided into multiple simpler 2D encoding operations, improving both the completeness of visual information representation and the manageability of processing complexity through structured division.
Solution Approach 2:
The system projects 3D point cloud coordinates onto multiple 2D planes, transforming the three-dimensional data representation into multiple two-dimensional views. This dimensional transformation enables efficient encoding by leveraging existing 2D video coding techniques while capturing comprehensive 3D spatial information through the combination of multiple projection angles.
3Adaptability or versatility
If omnidirectional video viewpoints are implemented, then user viewing experience improves, but the encoding and decoding complexity increases
Solution Approach 1:
The patent creates a universal encoding framework that generates multiple 2D projection planes (front, back, left, right, top, bottom views) from a single 3D point cloud data set. This multi-functional encoding approach enables the system to support various viewing angles and omnidirectional playback scenarios using the same encoded data structure, thereby achieving viewing angle flexibility without proportionally increasing encoding and decoding complexity.
Data Source
AI summary
A method of point cloud data processing includes determining a 3D region of a point cloud data and a 2D region of a point cloud track patch frame onto which one or more points of the point cloud data are projected; and reconstructing, based on patch frame data of the a point cloud track included in the 2D region and video frame data of corresponding point cloud component tracks, the 3D region of the point cloud data.