Point Cloud Denoising With Local-Global Patch Attention
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Solution Overview
Problem
Existing point cloud denoising methods, particularly for disordered point clouds, fail to effectively utilize multi-level patch information, resulting in poor denoising effects.
Innovation Solution
A point cloud denoising method based on multi-level attention perception, utilizing a neural network with a patch feature encoder, global level perception module, and multi-offset decoder to extract and adjust multi-scale patch features, and apply attention vectors for precise location offset calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data-driven denoising methods using neural networks are applied to scattered point clouds, then denoising effect is improved beyond previous methods, but multi-level patch information is not considered resulting in poor denoising effect
Solution Approach 1:
The patent segments the point cloud into multiple patches at different scales (local patches and global patches). The local patch extractor divides the point cloud into overlapping local patches, while the global patch extractor creates larger contextual patches. This segmentation allows the network to process information at multiple resolutions, improving denoising accuracy by capturing both fine-grained local features and broader global structures.
Solution Approach 2:
The patent introduces a multi-scale dimensional structure by creating patches at different spatial scales. The local patches capture immediate neighborhood information, while global patches capture broader contextual information. This multi-dimensional patch structure allows the network to perceive point cloud data from multiple spatial perspectives simultaneously, resolving the contradiction between simplicity and effectiveness.
2Manufacturing precision
If multi-scale patch information is incorporated into the neural network, then denoising quality is improved, but the network structure and processing complexity increase
Solution Approach 1:
The patent extracts patch information separately at different scales using dedicated extractors. The local patch extractor and global patch extractor independently process the point cloud to generate respective patch features. This extraction approach allows the network to obtain multi-scale information without requiring a completely redesigned architecture, thereby improving quality while controlling structural complexity.
Solution Approach 2:
The patent employs a unified patch embedding layer that processes both local and global patch features through the same subsequent network components (attention mechanisms, feature fusion layers). This multi-functional design allows the network to handle multiple types of patch inputs using a single processing pipeline, improving point cloud quality while avoiding proportional increases in overall network complexity.
3Measurement precision
If global and local neighborhood information are both considered, then denoising performance is enhanced, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial attention mechanisms that selectively process information from local and global patches based on their relevance. Rather than uniformly processing all patch information at full detail, the attention mechanism dynamically prioritizes important features while reducing processing of less critical information. This approach maintains high denoising performance by focusing computational resources on the most informative patches.
Solution Approach 2:
The patent introduces dynamic feature fusion that adaptively combines local and global patch features based on the specific characteristics of each point cloud region. The network dynamically adjusts the weight and processing depth for different patch types depending on local noise patterns and geometric complexity, optimizing processing time while maintaining enhanced denoising performance through context-aware adaptive computation.
Data Source
AI summary
The present disclosure provides a point cloud denoising method based on multi-level attention perception, including the following steps: constructing a data set of point cloud denoising; constructing a point cloud denoising neural network, including a patch feature encoder, a global level perception module, a global level attention module, and a multi-offset decoder module, and training a network model by using the data set of point cloud denoising; for input point cloud, separately obtaining a neighborhood patch of a point of each original data point, and inputting coordinates of each data point in the neighborhood patch of a point to a trained denoising neural network to obtain a location offset of each original point; and separately adjusting, based on the obtained location offset, a location corresponding to each original data point in the input point cloud, to complete point cloud denoising.