Point Cloud Compression Image Padding for Real-Time Transmission
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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 data volume and storage 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 cost and transmission time increase significantly
Solution Approach 1:
The patent segments the point cloud data into multiple patches, where each patch represents a localized portion of the 3D space. This segmentation allows the encoder to process and compress smaller regions independently, reducing the overall data volume for transmission while maintaining the completeness of spatial and attribute information across all patches. The receiver can reconstruct the full point cloud by assembling these segmented patches.
2Quantity of substance
If point cloud data is compressed using traditional methods, then data volume is reduced, but processing complexity and loss of detail increase
Solution Approach 1:
The patent transforms the 3D point cloud data into a 2D image representation by projecting points onto image planes with defined up, down, left, and right directions. This dimensionality change allows the use of efficient 2D image compression algorithms (such as JPEG or PNG) while preserving the essential spatial relationships and attribute information. The transformation maintains processing precision by preserving the geometric structure in the projected 2D space.
3Quantity of substance
If patch images are packed tightly without padding, then storage efficiency is improved, but video encoding performance deteriorates due to non-standard dimensions
Solution Approach 1:
The patent modifies the dimensions of patch images by adding padding to transform them into standard video dimensions (e.g., 32x32, 64x64, or other conventional sizes). This parameter change allows the use of optimized video encoding algorithms that are tuned for standard dimensions, significantly improving encoding speed and performance. The padding is strategically placed to maintain the integrity of the original point cloud data while enabling efficient processing.
4Productivity
If padding is added to patch images to achieve standard dimensions, then video encoding performance improves, but storage efficiency decreases due to increased data size
Solution Approach 1:
The patent applies partial padding to patch images, adding only the minimum necessary padding to reach standard video dimensions without excessive data expansion. This approach strikes a balance between improving video encoding performance and minimizing the increase in data size. The padding is applied selectively based on the specific dimensions of each patch, ensuring that encoding speed is enhanced while storage overhead is kept to a minimum.
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. 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, wherein pixel values for the padding are determined based on neighboring pixels values such that the padding is smoothed in the image frame. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud.