Neural Network Procedural Colorization for Point Cloud Data Compression
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Solution Overview
Problem
High-quality spatial data for immersive experiences in VR, AR, and MR requires significant bandwidth and memory, and existing technologies struggle to efficiently compress and optimize color information, especially when point cloud data lacks color information, leading to excessive data storage and transmission requirements.
Innovation Solution
A method involving a neural network that decompresses and reconstructs color information for point cloud data by using geometry information, residual color signals, and hint data, allowing for efficient compression and rendering of spatial data, enabling reduced data transmission while maintaining realistic colorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high quality spatial data is used for immersive experiences, then realism and visual quality are improved, but bandwidth and memory consumption increase excessively
Solution Approach 1:
The patent extracts only the essential color information (residual color signals) from the full color spatial data, separating it from the geometry data. This allows the system to transmit and store minimal color data while reconstructing the full color experience through neural network-based procedural colorization, thereby reducing data volume while maintaining visual quality
Solution Approach 2:
The patent transforms the color information from full RGB values to residual color signals that represent deviations from predicted colors. This parameter transformation reduces the amount of color data needed while preserving the ability to reconstruct high-quality color information through the neural network model
2Productivity
If color information is completely removed from spatial data to reduce bandwidth, then data transmission efficiency is improved, but color accuracy and realism are lost
Solution Approach 1:
The patent performs preliminary color prediction using a neural network model before transmission. The predicted colors serve as a baseline, and only the residuals (differences between actual and predicted colors) are transmitted. This preliminary action allows efficient compression while preserving color accuracy through residual correction
Solution Approach 2:
The patent implements a feedback mechanism where residual color signals are transmitted alongside geometry data, and the receiving system uses these residuals to correct the neural network's predicted colors. This feedback loop ensures that color accuracy is maintained despite transmitting minimal color information
3Device complexity
If spatial capturing sensors like lidars are used to capture only 3D positions without color information, then device complexity is reduced, but rendering realistic views becomes difficult
Solution Approach 1:
The patent introduces a neural network model as an intermediary that generates color information from geometry data. This intermediary component allows simple sensors like lidars to produce realistic colored visualizations by synthesizing color information that would otherwise require complex multi-sensor systems
Solution Approach 2:
The patent replaces the need for complex mechanical color-capture systems (multiple cameras, synchronized sensors) with a computational approach using neural networks. The neural network substitutes the physical sensing mechanism, generating color information algorithmically from geometric data alone
Data Source
AI summary
Systems and methods are described for compressing color information in point cloud data. In some embodiments, point cloud data includes point position information and point color information for each of a plurality of points. The point position information is provided to a neural network, and the neural network generates predicted color information (e.g. predicted luma and chroma values) for respective points in the point cloud. A prediction residual is generated to represent the difference between the predicted color information and the input point color position. The point position information (which may be in compressed form) and the prediction residual are encoded in a bitstream. In some embodiments, color hint data is encoded to improve color prediction.