Point Cloud Patch Compression for Storage Reduction
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Solution Overview
Problem
Point clouds captured by sensors like LIDAR systems and 3D cameras generate large amounts of data, making them costly and time-consuming to store and transmit, which limits their use in real-time applications and storage due to high storage requirements.
Innovation Solution
A system that compresses point cloud data by dividing it into patches, projecting points onto patch planes, and generating patch images with depth information, which are then packed into image frames and encoded using video encoding standards like HEVC or AV1, allowing for efficient storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is stored and transmitted in uncompressed form, then data integrity and quality are maintained, but storage requirements and transmission time increase significantly
Solution Approach 1:
The point cloud data is divided into multiple patches, where each patch represents a portion of the original point cloud. This segmentation allows for independent processing and compression of different regions, enabling efficient storage while maintaining the ability to reconstruct the complete point cloud data from the compressed patches
Solution Approach 2:
The patent transforms 3D point cloud data into 2D patch representations by projecting points onto patch planes. This dimensionality reduction from 3D to 2D significantly decreases the data volume required for storage and transmission, while the depth information is preserved through separate encoding to maintain spatial accuracy
2Quantity of substance
If point cloud data is compressed to reduce storage needs, then storage requirements and transmission time decrease, but processing complexity increases
Solution Approach 1:
By dividing the point cloud into patches, the processing complexity is distributed across multiple smaller units rather than processing the entire point cloud as one large dataset. This segmentation enables parallel processing and reduces the computational burden on individual processing units
Solution Approach 2:
The transformation to 2D patch representations simplifies the data structure for storage and transmission. The regular 2D grid structure of patches is more amenable to standard compression algorithms and data structures compared to unstructured 3D point cloud data
3Speed
If point cloud data is compressed for real-time transmission, then transmission speed increases, but compression and decompression time increase
Solution Approach 1:
The patch-based segmentation enables parallel compression of multiple patches simultaneously, reducing the total compression time. Each patch can be compressed independently and in parallel with other patches, significantly speeding up the overall compression process while maintaining compression efficiency
Solution Approach 2:
The patent performs preprocessing steps such as patch extraction and projection before the main compression process. By organizing the data into patches and projecting them onto 2D planes in advance, the subsequent compression operations can proceed more efficiently with pre-processed, structured data
Data Source
AI summary
A system comprises an encoder configured to compress attribute information and/or spatial information for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. The encoder is configured to convert a point cloud into an image based representation. The encoder packs patch images into an image frame and fills empty spaces in the image frame with a padding. Various compression strategies may be used to encode an occupancy map and related block-to-patch information indicating which portions of the image frame correspond to which packed patches. Packed image frames comprising patches and padding, along with an encoded occupancy map and related block-to-patch information are sent to a decoder. The decoder is configured to generate a decompressed point cloud based on the packed image frames, compressed occupancy map, and related block-to-patch information.