Neural Network Point Cloud Alignment via Gaussian Mixture Models
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Solution Overview
Problem
Computer vision tasks, such as point cloud registration, require significant memory and computing resources, and existing methods struggle to efficiently align visual data from multiple frames or sources, leading to suboptimal performance.
Innovation Solution
A neural network-based system that uses a learned geometric representation to align point clouds by training a network to generate a Gaussian mixture model and compute registration transforms, enabling efficient alignment and reduction of computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used for point cloud registration, then alignment accuracy can be achieved, but memory and computing resources are consumed excessively
Solution Approach 1:
The patent replaces traditional mechanical/computational geometry methods with a neural network-based system. The neural network learns geometric representations and registration transforms from data, substituting conventional iterative optimization algorithms with a trained model that directly computes alignments, thereby reducing computational resource requirements while maintaining accuracy
Solution Approach 2:
The patent changes the approach by transforming the problem from computing geometric transforms directly to learning representations and transforms through neural networks. By training the network on paired data (point clouds and their registrations), the system learns optimal parameters for alignment, reducing the need for resource-intensive traditional computation during inference
2Measurement precision
If traditional methods are used for point cloud registration, then alignment can be performed, but processing time is excessive
Solution Approach 1:
The neural network is pre-trained offline using paired point cloud data and their corresponding registration transforms. During inference, the trained network rapidly computes alignments without requiring time-consuming iterative optimization, as the heavy computational work was performed in advance during training
Solution Approach 2:
The patent substitutes slow iterative geometric optimization with a pre-trained neural network that provides fast direct computations. The network's learned representations enable rapid alignment calculations, dramatically reducing processing time compared to traditional methods
3Use of energy by moving object
If neural networks are used to learn geometric representations, then computing resources are reduced, but network complexity increases
Solution Approach 1:
The neural network architecture is designed to perform multiple functions: learning geometric representations of point clouds, computing registration transforms, and handling variations in input data. This multi-functionality consolidates what would otherwise require separate complex systems into a single unified model, managing complexity while reducing overall resource requirements
Data Source
AI summary
Apparatuses, systems, and techniques to generate a 3D model of an object. In at least one embodiment, a 3D model of an object is generated by one or more neural networks, based on a plurality of images of the object.