Point Cloud Compression Rate Control With Learned Displacement Refinement
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Solution Overview
Problem
Existing point cloud compression technologies face challenges with hard rate control mechanisms that introduce aliasing artifacts and loss of points, particularly in naive hard rate control methods, limiting their effectiveness in achieving a wide rate range without compromising quality.
Innovation Solution
A learning-based hard rate control mechanism that utilizes a neural network to produce predicted displacements based on feature maps and additional bitstreams, allowing for a wider rate range while minimizing artifacts by refining the reconstruction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If naive hard rate control is used to achieve wider rate range, then rate control flexibility is improved, but aliasing artifacts and point loss increase
Solution Approach 1:
The patent introduces a learning-based displacement prediction module as an intermediary between the hard rate control mechanism and the final reconstruction. This mediator uses neural networks to predict and compensate for displacements caused by hard rate control, thereby reducing aliasing artifacts while maintaining the benefits of wide rate control range.
Solution Approach 2:
The patent replaces the traditional mechanical/mathematical hard rate control approach with a learning-based system that uses neural networks to predict displacements. This substitution allows the system to achieve the same rate control function while intelligently compensating for artifacts, thus resolving the contradiction between rate control flexibility and artifact reduction.
2Adaptability or versatility
If naive hard rate control is used to achieve wider rate range, then rate control flexibility is improved, but reconstruction quality deteriorates
Solution Approach 1:
The learning-based displacement prediction module serves as a mediator that processes the output of hard rate control and refines it by predicting and compensating for displacement errors. This intermediary step maintains the flexibility of wide rate control while improving reconstruction quality through intelligent correction.
Solution Approach 2:
The patent implements a feedback mechanism where the neural network analyzes the features from hard rate control and generates displacement predictions that are fed back to correct the reconstruction. This feedback loop enables the system to maintain high reconstruction quality across a wide range of rate control settings.
3Device complexity
If traditional hard rate control is used, then device complexity is reduced, but artifact reduction capability is insufficient
Solution Approach 1:
The patent adds a learning-based displacement prediction module as an intermediary component that works in conjunction with traditional hard rate control. This additional module, while increasing complexity slightly, provides significant artifact reduction capability by intelligently predicting and compensating for displacement errors.
Solution Approach 2:
The patent enhances the traditional mechanical hard rate control system by substituting part of its functionality with a learning-based neural network approach. This substitution maintains the simplicity of the overall system architecture while introducing advanced artifact reduction capabilities through learned displacement predictions.
Data Source
AI summary
Some embodiments of a method may include: obtaining a bitstream and an already-decoded set of coordinates; generating a feature map by using the already-decoded set of coordinates; using a neural network to produce one or more predicted displacements from each feature in the feature map; and adding the one or more predicted displacements to the already decoded set of coordinates to obtain a final reconstruction. Some embodiments of a method may include: obtaining a plurality of per-point residuals between an original and a downscaled point cloud; using a neural network to obtain a feature for each residual; pooling the features according to a geometry of the downscaled point cloud; and encoding the pooled features into a bitstream.