Point Cloud Super-Resolution With Joint Geometry-Attribute Upsampling
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Solution Overview
Problem
Existing point cloud super-resolution methods primarily focus on upsampling geometry information while neglecting attribute information, failing to account for the different importance of points in the topology and lacking exploration of mutual correlations between geometry and attribute information, and are limited in handling real-world point clouds and downstream tasks.
Innovation Solution
A method for joint super-resolution of point cloud geometry and attribute information using sparse tensor-based convolution networks, progressive upsampling with BCE loss constraints, and learning-based attribute refinement, combined with existing codecs to enhance encoding and decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only geometry information is upsampled using conventional methods, then geometry resolution is improved, but attribute information quality deteriorates and is neglected
Solution Approach 1:
The patent combines geometry upsampling and attribute upsampling into a unified joint super-resolution framework. The geometry encoder and attribute encoder work together to process both types of information simultaneously, ensuring that improvements in geometry resolution do not come at the expense of attribute quality. This merging approach allows the system to leverage spatial correlations between geometry and attributes for mutual enhancement.
Solution Approach 2:
The neural network architecture is designed to handle multiple functions: geometry encoding, attribute encoding, and their respective upsampling operations within a single unified model. This multi-functional design enables the system to process different types of point cloud information (geometry and attributes) through shared computational resources and coordinated processing stages.
2Productivity
If conventional upsampling methods are used, then processing speed is maintained, but the ability to handle real-world point clouds and downstream tasks deteriorates
Solution Approach 1:
The patent applies local quality enhancement by processing different regions of the point cloud with appropriate attention to their specific characteristics. The neural network learns to identify and preserve important local features in real-world point clouds while maintaining overall processing efficiency. This localized processing approach improves adaptability to diverse real-world scenarios without sacrificing productivity.
3Measurement precision
If joint super-resolution of geometry and attribute is implemented, then overall point cloud quality is improved, but computational complexity increases
Solution Approach 1:
The joint super-resolution process is segmented into distinct functional modules: geometry encoding, attribute encoding, geometry upsampling, and attribute upsampling. Each module handles a specific aspect of the processing, which reduces the computational burden of the overall system while maintaining the benefits of joint processing. This segmentation allows for optimized resource allocation and more manageable complexity.
Data Source
AI summary
Embodiments of the present disclosure provide a solution for point cloud processing. A method for point cloud processing is proposed. The method comprises: upsampling first geometry information of a first point cloud (PC) sample; upsampling first attribute information of the first PC sample; and determining second geometry information and second attribute information of a second PC sample based on the upsampled first geometry information and the upsampled first attribute information, wherein the second PC sample corresponds to the first PC sample, and a resolution of the second PC sample is higher than a resolution of the first PC sample.