Point Cloud Encoding Prediction Residuals Sparse Data
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Solution Overview
Problem
The direct coding mode (DCM) for encoding point cloud data, particularly in sparse point scenarios like LiDAR data, results in reduced encoding efficiency due to its uncompressed nature, leading to increased data processing loads.
Innovation Solution
An information processing device and method that predicts position information of points in a point cloud using a reference point, derives differences between predicted and actual positions, and encodes these differences to generate a bitstream, thereby reducing the data amount processed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If direct coding mode (DCM) is used for encoding sparse point cloud data, then the encoding process is simplified, but the encoding efficiency deteriorates due to uncompressed processing
Solution Approach 1:
The patent applies preliminary action by performing prediction of point positions before encoding. The prediction unit predicts positions of points in sparse regions based on reference points from dense regions, and the difference derivation unit calculates residuals before encoding. This preliminary prediction step enables more efficient encoding by reducing the data that needs to be compressed, thereby resolving the contradiction between encoding simplicity and encoding efficiency.
2Adaptability or versatility
If all points in sparse point cloud data are processed by DCM, then the processing coverage is complete, but the data amount increases leading to reduced encoding efficiency
Solution Approach 1:
The patent applies local quality by differentiating processing methods for different regions of the point cloud. Dense regions are processed using traditional octree encoding, while sparse regions are processed using prediction-based encoding. This region-specific approach reduces the overall data amount by applying appropriate compression strategies to each local area, thereby resolving the contradiction between complete processing coverage and data amount reduction.
Solution Approach 2:
The patent segments the point cloud data into dense regions and sparse regions, applying different encoding strategies to each segment. By dividing the data and applying specialized prediction-based encoding only to sparse regions, the system reduces the total data amount while maintaining complete processing coverage across all regions.
3Speed
If uncompressed processing is used for sparse points, then the processing speed is maintained, but the information amount remains high reducing encoding efficiency
Solution Approach 1:
The patent extracts only the essential information from sparse points by predicting their positions and encoding only the residuals (differences between predicted and actual positions). This extraction approach reduces the information amount that needs to be stored and transmitted while maintaining processing speed, as the prediction operation is computationally efficient and the residual encoding requires less data to be processed.
Data Source
AI summary
The present disclosure relates to an information processing device and a method capable of suppressing a reduction in encoding efficiency of point cloud data. As for a point cloud representing an object having a three-dimensional shape as a point group, position information of a point to be processed is predicted on the basis of position information of a reference point, position information of a prediction point is generated, a difference between the generated position information of the prediction point and the position information of the point to be processed is derived, the derived difference is encoded, and a bitstream is generated. The present disclosure may be applied to, for example, an information processing device, an electronic device, an information processing method, a program or the like.