Point Cloud Interpolation Decoding for Lower-Bitrate 3D Compression
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Solution Overview
Problem
Point clouds require significant bandwidth for transmission due to their large bitrate, and existing video codecs are not effective in encoding and decoding 3D video content, leading to high computational costs and hardware expenses.
Innovation Solution
Convert point clouds into a 2D state by projecting them onto multiple frames, using existing video codecs to compress and reconstruct the 2D frames, and then transmit the bitstream for decoding and rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional point cloud compression methods are used, then implementation complexity is reduced, but compression efficiency is insufficient
Solution Approach 1:
The point cloud data is segmented into multiple patches, and each patch is processed independently through an autoencoder network. This segmentation enables parallel processing and improves compression efficiency without requiring complex global optimization algorithms.
Solution Approach 2:
The patent applies K-means clustering to pre-group points into patches before compression processing. This preliminary organization of data structure enables more efficient subsequent encoding and decoding operations, improving overall compression performance.
2Measurement precision
If more bits are allocated to each point, then point cloud quality is improved, but bitrate consumption increases
Solution Approach 1:
The patent transforms point cloud data from spatial coordinates into latent space representations through autoencoder networks. This parameter transformation enables more efficient bit allocation, achieving high reconstruction quality at lower bitrates by capturing essential features in the latent space rather than storing raw coordinates.
Solution Approach 2:
The autoencoder network acts as an intermediary that compresses point cloud data into a compact latent representation. This intermediate form retains essential geometric information while requiring fewer bits for storage and transmission, enabling high quality reconstruction with reduced bitrate.
3Productivity
If existing compression schemes are applied, then implementation is simpler, but they do not take advantage of 3D structure and are outperformed by 2D image codecs
Solution Approach 1:
The patent processes 3D point cloud data by organizing points into 2D-like patches while preserving 3D spatial relationships through coordinate transformations and depth information. This approach enables the use of efficient 2D image coding techniques while maintaining 3D structural integrity, achieving superior compression compared to traditional 3D methods.
Data Source
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Figure 3
Figure 4A
AI summary
A method for decoding point cloud includes decoding the compressed bitstream into a geometry frame representing geometry of a 3D point cloud and a texture frame representing texture of the 3D point cloud, the geometry frame and the texture frame including pixels representing points of the 3D point cloud from different layers of the 3D point cloud; deriving a set of missing geometry values from pixels in the geometry frame and a set of missing texture values from pixels in the texture frame; generating a first set of frames representing geometry from the different layers based on the geometry frame and the set of missing geometry values; generating a second set of frames representing the texture from the different layers based on the texture frame and the set of missing texture values; and generating the 3D point cloud using the first set of frames and the second set of frames.