Pyramid Transformer for Point Cloud Reconstruction Precision
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Solution Overview
Problem
Traditional three-dimensional reconstruction methods, including deep learning approaches, face challenges in achieving high reconstruction precision for object surface details, especially for reflective objects and those with less texture, due to their focus on local features and limited consideration of multi-scale information.
Innovation Solution
A point cloud reconstruction method based on a pyramid transformer that processes multiple views of an object to extract both local and global features, using a dual-channel pyramid network with attention mechanisms to update point features across views, enabling more accurate sampling and optimization of network parameters for improved precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional three-dimensional reconstruction methods are used, then the reconstruction process can be completed, but the reconstruction precision of object surface details is relatively low
Solution Approach 1:
The patent divides the feature extraction process into multiple scales using a pyramid network structure with different resolution levels. Each level extracts features at a specific scale, and these multi-scale features are then fused to achieve both global structure and local detail reconstruction, resolving the contradiction between overall reconstruction and surface detail precision.
Solution Approach 2:
The patent transforms the feature extraction process by adding a scale dimension through the pyramid network. Instead of processing features at a single scale, the system processes features across multiple scales (from coarse to fine), enabling the network to capture both global geometric structure and local surface details simultaneously.
2Productivity
If deep learning methods are applied to three-dimensional reconstruction, then fewer input images are required, but the reconstruction precision of object surface details remains insufficient
Solution Approach 1:
The patent segments the feature extraction process into multiple scales using a pyramid network structure with different resolution levels. Each level extracts features at a specific scale, and these multi-scale features are then fused to achieve both global structure and local detail reconstruction, resolving the contradiction between overall reconstruction and surface detail precision.
3Device complexity
If only local features of a single scale are considered, then the processing is simpler, but the reconstruction precision of object surface details is relatively low
Solution Approach 1:
The patent divides the feature extraction process into multiple scales using a pyramid network structure with different resolution levels. Each level extracts features at a specific scale, and these multi-scale features are then fused to achieve both global structure and local detail reconstruction, resolving the contradiction between overall reconstruction and surface detail precision.
Solution Approach 2:
The patent transforms the feature extraction process by adding a scale dimension through the pyramid network. Instead of processing features at a single scale, the system processes features across multiple scales (from coarse to fine), enabling the network to capture both global geometric structure and local surface details simultaneously.
Data Source
AI summary
A point cloud reconstruction method and apparatus based on a pyramid transformer, a device, and a medium are provided. The method includes: after obtaining point feature data corresponding to each view, inputting the point feature data into a dual-channel pyramid network; when passing through a first channel, inputting an updated output of an attention mechanism of each layer to a next layer, when passing through a second channel, splicing and inputting the updated output of the attention mechanism of each layer and the updated output of the attention mechanism of a corresponding layer of the first channel in a same layer as the next layer to the next layer; converting the updated output of the attention mechanism of a bottommost layer of the second channel into point cloud coordinates to obtain a point cloud result, and taking an intersection of the point cloud result to characterize a target object.