Spinning Sensor Point Cloud Geometry Coding With Residual-Radius Prediction
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Solution Overview
Problem
Existing point cloud codecs fail to efficiently compress sparse geometry data captured by a spinning sensor head, requiring simple and low-latency encoding for applications like autonomous driving, due to suboptimal prediction schemes that introduce high bit costs and latency.
Innovation Solution
A dynamic list of prediction data is used to derive candidate predictors for encoding and decoding point cloud data, updating prediction data based on residual radius information to improve compression efficiency and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing point cloud codecs use traditional prediction schemes for sparse geometry data, then encoding simplicity is maintained, but compression efficiency deteriorates with high bit costs
Solution Approach 1:
The patent applies dynamics by transitioning from static prediction schemes to dynamic prediction schemes that adapt to the specific characteristics of sparse point cloud data. The encoder dynamically selects prediction modes and updates prediction states based on the actual data being encoded, allowing the system to optimize compression efficiency for each specific scene while maintaining manageable complexity through structured adaptation rules.
Solution Approach 2:
The patent changes parameters by introducing multiple prediction modes (e.g., different predictor types, prediction ranges, and update strategies) that can be selected and adjusted based on the local characteristics of the point cloud data. This parameter adaptation allows the system to achieve better compression by matching the prediction strategy to the actual data distribution, reducing the bits needed to represent the geometry.
2Loss of information
If complex prediction schemes are used to improve compression performance, then bit costs are reduced, but encoding latency increases
Solution Approach 1:
The patent segments the prediction process into distinct, manageable stages: initial prediction using predefined modes, residual calculation, and selective state updates. This segmentation allows the encoder to process data in a structured flow that minimizes unnecessary computations, reducing latency while maintaining the compression benefits of adaptive prediction through targeted updates only where needed.
Solution Approach 2:
The patent applies partial action by implementing selective prediction state updates rather than updating all prediction states at every step. The system performs prediction updates only when beneficial, based on thresholds or specific conditions, avoiding the excessive computation that would occur with full updates. This partial action maintains compression efficiency by updating states when it reduces bit cost, while avoiding unnecessary updates that would increase latency.
3Loss of time
If simple encoding methods are used for real-time processing, then latency is reduced, but compression performance deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-defining a set of prediction modes and structures that are optimized for common sparse point cloud patterns. These pre-configured prediction schemes are ready to be applied immediately without requiring complex real-time analysis, enabling fast encoding while achieving good compression performance through the use of these pre-optimized prediction strategies tailored to spinning sensor head data characteristics.
4Loss of information
If adaptive prediction schemes are implemented to improve compression, then bit costs are reduced, but device complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the encoder monitors the effectiveness of prediction modes and adjusts subsequent prediction strategies based on observed performance. This feedback loop allows the system to adapt to the specific characteristics of the input data, achieving lower bit costs by selecting the most effective prediction modes for each local region, while managing complexity through structured feedback rules that prevent unbounded adaptation.
Data Source
AI summary
A method of encoding/decoding a point cloud into/from a bitstream of encoded point cloud data representing a physical object includes determining a dynamic list of at least one prediction data used to derive at least one candidate predictor used to encode geometry data of points of the point cloud. The list of at least one prediction data is dynamic because during the encoding of points, prediction data are updated based on residuals radius of decoded points.