Task-Driven Neural Network Compression for Point Cloud Geometry
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Solution Overview
Problem
Current methods for compressing point clouds, such as those used in 3D graphics and VR/AR applications, face challenges in achieving efficient data transmission due to the large volume of data required to maintain high fidelity representations of 3D objects and scenes.
Innovation Solution
A task-driven machine learning-based compression scheme that optimizes neural networks for specific tasks, allowing for efficient compression of point cloud geometry by conditioning the codec on the intended use of the reconstructed signal, thereby achieving better compression rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional video coding or octree-based methods are used for point cloud compression, then the transmission of large amounts of 3D data becomes feasible, but the compression efficiency and fidelity for specific applications are insufficient
Solution Approach 1:
The patent applies dynamics by making the neural network architecture and processing pipeline adaptive to different application tasks. The system dynamically adjusts the encoding and decoding processes based on the specific task requirements (e.g., rendering, segmentation, detection), allowing the compression scheme to optimize for each application's unique needs while maintaining high fidelity where required.
Solution Approach 2:
The patent changes parameters by using task-specific conditioning to modify the neural network's behavior during encoding and decoding. Different tasks (rendering, segmentation, detection) require different parameter configurations in the neural network, allowing the system to optimize compression efficiency and reconstruction quality according to the specific application requirements.
2Measurement precision
If high-fidelity point cloud representation is maintained with thousands or millions of points, then the accuracy of 3D object representation is improved, but the data transmission burden increases significantly
Solution Approach 1:
The patent replaces traditional mechanical compression systems (octree-based geometric coding, video projection methods) with a neural network-based system. This substitution allows for more efficient representation of point cloud data by learning underlying patterns and relationships, achieving high fidelity with reduced data volume through intelligent compression rather than brute-force representation.
Solution Approach 2:
The patent uses composite approaches by combining multiple neural network components (encoder, decoder, task-specific modules) and integrating different processing strategies. This composite neural network architecture enables the system to maintain high representation fidelity while achieving efficient compression by leveraging the strengths of different network components working together.
3Productivity
If task-driven neural network conditioning is applied for application-specific optimization, then compression efficiency for specific tasks is improved, but the complexity of the encoding system increases
Solution Approach 1:
The patent applies universality by designing a single neural network-based compression framework that can handle multiple tasks (rendering, segmentation, detection) through task-specific conditioning. Rather than requiring separate specialized systems for each application, the universal neural network architecture adapts to different tasks by adjusting its processing parameters, reducing overall system complexity while maintaining task-specific optimization.
4Quantity of substance
If implicit representation using occupancy networks is used, then the representation efficiency and scalability are improved, but the training and compression process becomes more complex
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network models on large datasets before deployment for compression. The occupancy networks are trained in advance to learn effective implicit representations of 3D data, and this pre-trained knowledge is then leveraged during the compression process. This preliminary training phase separates the complex learning task from the actual compression operation, making the overall system more manageable.
Data Source
AI summary
Methods, systems and devices described herein implement a task-driven machine learning-based compression scheme for point cloud geometry implicit representation. The machine learning-based codec is able to be optimized for a task to achieve better compression rates by being conditioned to what the reconstructed signal will be used for. The latent representation of the point cloud or the neural network that implicitly represents the point cloud itself are able to be compressed. The methods described herein perform efficient compression of the implicit representation of a point cloud given a target task.