Patch-Based Point Cloud Bitstream Structure for Low-Delay Streaming
Find Innovative SolutionsGenerate Solutions
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 and attribute information, and packing these images into video frames with PCCNAL units for efficient encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is captured and stored in full detail, then data completeness and quality are improved, but storage space requirements 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 the encoder to process and compress each patch independently, reducing the overall data volume while maintaining the completeness of the full point cloud representation through the aggregation of all patches.
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 associated depth information, significantly reducing data volume while preserving the essential geometric and attribute information through the image representation.
2Productivity
If point cloud data is compressed to reduce storage space, then transmission speed and storage efficiency are improved, but data quality and reconstruction accuracy may deteriorate
Solution Approach 1:
The patent employs video compression standards (such as H.264/AVC or H.265/HEVC) that use adjustable compression parameters including quantization step size, transformation block size, and prediction modes. By optimizing these parameters, the system achieves high compression ratios while maintaining reconstruction accuracy within acceptable thresholds for the application.
Solution Approach 2:
The compression and decompression process is evaluated using quantitative metrics such as point cloud distance metrics (Chamfer distance, Hausdorff distance) and attribute reconstruction errors. This feedback mechanism allows for iterative optimization of compression parameters to achieve the desired balance between compression ratio and reconstruction accuracy.
3Ease of manufacture
If traditional compression methods are used for point cloud data, then implementation simplicity is maintained, but compression efficiency and processing speed remain insufficient for real-time applications
Solution Approach 1:
The patent leverages existing video compression standards and tools (such as FFmpeg, x264, x265) that are widely available and well-optimized. By reusing these established codecs for compressing the generated patch images, the system achieves high compression efficiency and processing speed without developing custom compression algorithms, thus maintaining implementation simplicity while enabling real-time processing.
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. 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, a bit stream structure may be used to communicate compressed point cloud data. The bit stream structure may include point cloud compression network abstraction layer (PCCNAL) units that enable use of groups of frames (GOFs), frame, and sub-frame signaling of patch information. Such a bit stream structure may permit low delay streaming and random access reconstruction of point clouds amongst other applications.