Neural Network Gap Filling for Point Cloud Surface Reconstruction
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Solution Overview
Problem
Conventional laser scanner systems produce point clouds with gaps between scan points, resulting in an unpleasant and inaccurate visual representation of closed surfaces, as the empty spaces become visually evident, disrupting the illusion of a continuous surface.
Innovation Solution
A neural network is trained using pairs of closed and non-closed surface point clouds to generate two-dimensional images, filling gaps in the non-closed surfaces and de-noising point cloud data to create a more realistic impression of closed surfaces, leveraging AI and machine learning to reduce processing resources and improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional laser scanner systems are used to scan objects, then scan data can be collected, but gaps between scan points are produced resulting in inaccurate visual representation
Solution Approach 1:
A neural network model is introduced as an intermediary component between the raw scan data and the final visual representation. The neural network processes the point cloud data and generates filled-in surface representations, acting as a mediator that transforms discrete scan points into continuous visual outputs without requiring traditional post-processing methods.
Solution Approach 2:
The patent replaces traditional mechanical or algorithmic gap-filling methods with a neural network-based system. Instead of using conventional image processing techniques or geometric algorithms to interpolate missing data, the system employs trained neural networks that learn from example data to predict and fill gaps in the scan points, substituting mechanical processing with intelligent computation.
2Manufacturing precision
If neural network training is performed using pairs of closed and non-closed surface point clouds, then gap filling capability is improved, but processing resources are consumed
Solution Approach 1:
The neural network model is trained in advance using pairs of closed and non-closed surface point clouds before actual gap filling operations are performed. This preliminary training phase allows the system to learn the characteristics of complete surfaces and develop accurate gap prediction capabilities, so that during actual processing, the trained model can quickly generate filled-in surfaces without requiring extensive computational resources.
Solution Approach 2:
The training process uses copies of point cloud data representing both closed and non-closed surfaces. By training the neural network on these replicated datasets, the system learns to recognize patterns and generate accurate gap fillings. The use of training copies allows the model to internalize surface completion rules without needing to process every real-world scan point in detail during inference.
3Ease of manufacture
If gaps between scan points are filled in using the trained neural network, then visual appearance is improved, but processing time is required
Solution Approach 1:
The trained neural network performs gap filling autonomously without requiring manual intervention or complex post-processing workflows. The system automatically processes the point cloud data, identifies gaps, and generates filled-in surfaces using the trained model, making the process self-service oriented and eliminating the need for additional manual steps that would increase processing time.
Data Source
AI summary
An example method for training a neural network includes generating a training data set of point clouds. The training data set includes pairs of closed surfaces point clouds and non-closed surfaces point clouds. The method further includes, for each of the closed surface point clouds and the non-closed surface point clouds, generating a two-dimensional (2D) image by rendering a three-dimensional scene. The 2D image for the non-closed surfaces point clouds includes a gap in a surface, and the 2D image for the closed surfaces point clouds are free of gaps. The method further includes training the neural network to generate a trained neural network. The method further includes filling, using the trained neural network, gaps between scan points of the 2D image, and de-noising, using the trained neural network, scan point cloud data to generate a closed surface image of the scan point cloud data.