Point Cloud Compression via Multi-Resolution Video Encoding
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Solution Overview
Problem
Point clouds generated by sensors like LIDAR systems are large and costly to store and transmit, limiting their use in real-time applications due to high storage and network resource requirements.
Innovation Solution
A system that compresses point cloud data by projecting points onto patch planes, generating patch images with spatial and attribute information, and using video encoding standards like HEVC to pack and decode these images efficiently, allowing for real-time transmission and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is stored and transmitted in its original format, then complete spatial and attribute information is preserved, but storage requirements and network resource consumption increase significantly
Solution Approach 1:
The point cloud data is divided into multiple patches, where each patch represents a subset of points with associated spatial and attribute information. This segmentation allows selective encoding and transmission of different regions, reducing overall data volume while maintaining the ability to reconstruct complete point cloud information when all patches are combined.
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 coordinates (x, y, z) into image coordinates (u, v) with depth information, enabling efficient compression using 2D image encoding techniques while preserving essential 3D spatial relationships.
2Productivity
If point cloud data is compressed using traditional methods, then storage and transmission costs are reduced, but real-time processing capability is limited
Solution Approach 1:
The point cloud data undergoes preliminary organization into patches and projection into 2D images before compression. This pre-processing step structures the data in a format optimized for video encoding, enabling faster real-time compression and transmission without requiring heavy computational resources during the actual encoding process.
3Quantity of substance
If video encoding standards like HEVC are used to compress patch images, then compression efficiency is improved, but encoding complexity increases
Solution Approach 1:
The patent leverages existing video encoding standards (HEVC, H.264, VP9, AV1) that are already widely implemented and optimized in hardware and software. By mapping point cloud patches to standard video image formats, the system可以利用 these universal encoding tools, achieving high compression efficiency without developing custom complex encoding algorithms.
Data Source
AI summary
A system comprises an encoder configured to compress attribute 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. 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. In some embodiments, an encoder performs downscaling of an image frame prior to video encoding and a decoder performs upscaling of an image frame subsequent to video decoding.