Dynamic Point Cloud Encoding via Depth Image Segmentation
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Solution Overview
Problem
Existing methods for compressing and distributing dynamic point clouds are inefficient, leading to high bit-rate and storage requirements while maintaining acceptable quality, which hinders practical distribution in applications like autonomous vehicles and virtual reality.
Innovation Solution
A two-layer based point cloud encoding and decoding method that pads depth images to represent nearer and farther points, allowing for efficient encoding and decoding of dynamic point clouds by time-interleaving and filtering pixel values, using existing video codecs to compress geometry and texture information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing compression methods are used for dynamic point clouds, then quality of experience is maintained, but bit-rate and storage requirements become excessively high
Solution Approach 1:
The patent segments the point cloud data by depth, dividing it into multiple depth layers (near, mid, far regions). Each layer is processed independently with appropriate compression strategies, allowing efficient representation of different spatial regions while reducing overall bit-rate requirements compared to uniform compression of the entire point cloud
Solution Approach 2:
The patent transforms 3D point cloud data into 2.5D depth images by projecting points onto a 2D plane while preserving depth information. This dimensional transformation enables the use of efficient 2D video compression codecs while maintaining the essential 3D structure, significantly reducing storage and transmission requirements
2Quantity of substance
If point cloud data is compressed to reduce bit-rate, then storage and transmission efficiency improves, but reconstruction quality deteriorates
Solution Approach 1:
The patent applies different compression strategies to different depth regions based on their importance. Near and mid-depth regions that are more critical for perception are preserved with higher quality, while far-depth regions use more aggressive compression. This local quality differentiation maintains reconstruction quality for important regions while reducing overall bit-rate
Solution Approach 2:
The patent introduces depth images as an intermediary representation between the original 3D point cloud and the compressed output. These depth images serve as a compact intermediate form that captures essential geometric information, enabling efficient compression while preserving reconstruction quality through the depth map structure
3Productivity
If traditional compression algorithms are used, then implementation is straightforward, but processing efficiency and compression ratio are insufficient
Solution Approach 1:
The patent leverages existing video compression codecs (such as H.264, H.265, or AV1) that are already widely implemented and optimized. By adapting point cloud data into a format suitable for these universal codecs through depth image transformation, the patent achieves high compression efficiency without requiring entirely new compression algorithms, balancing productivity improvement with acceptable complexity
Data Source
AI summary
At least one embodiment relates to a method for padding a first depth image representative of depth values of nearer points of a point cloud frame and a second depth image representative of depth values of farther points of a point cloud frame. The method also comprises encoding a video stream including a time-interleaving of said encoded first and second images. There is also provided a method comprising decoding a video stream to provide a first depth image representative of depth values of nearer points of a point cloud frame and a second depth image representative of depth values of farther points of a point cloud frame; and filtering pixel values of the second depth image by using pixel values of the first depth image.