Point Cloud Compression Using Patch Images and Tiling Metadata
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Solution Overview
Problem
Point clouds captured by sensors like LIDAR systems and 3-D cameras contain large amounts of data, making storage and transmission costly and time-consuming, limiting real-time applications.
Innovation Solution
A system that compresses point cloud data by projecting points onto patch planes, generating patch images with depth information, packing these images into frames, and using video encoding techniques, along with supplementary messages for efficient decoding and region of interest access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is stored and transmitted in uncompressed form, then data quality and completeness are maintained, but storage costs and transmission time increase significantly
Solution Approach 1:
The point cloud data is divided into multiple patches, where each patch represents a local region of the point cloud. This segmentation allows for independent processing and compression of each patch, reducing overall transmission time while maintaining local data quality. The encoder processes patches separately and transmits them independently, enabling parallel transmission and faster overall delivery.
Solution Approach 2:
The patent transforms 3D point cloud data into 2D patch images by projecting points onto a 2D plane. This dimensionality reduction converts spatial coordinate data (x, y, z) into image format data, which can be compressed using efficient 2D image compression algorithms, significantly reducing data size and transmission time while preserving visual quality.
2Quantity of substance
If point cloud data is compressed using traditional methods, then data size is reduced, but compression efficiency is insufficient for large-scale point clouds
Solution Approach 1:
The point cloud is divided into multiple patches that can be processed independently and in parallel. This segmentation enables the compression system to handle large-scale point clouds more efficiently by distributing the compression workload across multiple processing units, thereby improving overall compression productivity while achieving significant data size reduction.
Solution Approach 2:
The patent replaces traditional point cloud compression methods with video encoding technology. By converting point cloud patches into image format and applying established video compression standards, the system achieves superior compression efficiency and higher productivity compared to conventional approaches, while significantly reducing the final data size.
3Productivity
If video encoding technology is applied to point cloud compression, then compression efficiency improves, but implementation complexity increases
Solution Approach 1:
The patent leverages the universality of video encoding technology, which is a well-established and widely available solution. By adapting existing video compression standards for point cloud data, the system achieves high compression efficiency without requiring entirely new algorithms or hardware, thereby limiting the increase in implementation complexity despite improved productivity.
4Speed
If point clouds are processed in real-time, then application responsiveness improves, but storage and network resource requirements increase
Solution Approach 1:
By dividing point cloud data into smaller patches, the system enables parallel processing of multiple patches simultaneously, improving real-time processing speed. The segmented structure allows for selective transmission and storage of only necessary patches, reducing overall storage resource requirements while maintaining responsive performance for real-time applications.
Solution Approach 2:
The transformation of 3D point cloud data into 2D patch images reduces the dimensional complexity and data volume, enabling more efficient real-time processing with lower storage requirements. The 2D image representation maintains essential visual information while occupying less storage space, facilitating real-time application performance.
Data Source
AI summary
A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. Additionally, an encoder is configured to signal and/or a decoder is configured to receive a supplementary message comprising volumetric tiling information that maps portions of 2D image representations to objects in the point. In some embodiments, characteristics of the object may additionally be signaled using the supplementary message or additional supplementary messages.