4D Spatio-Temporal Model Generation Using Neural Network Skeletons
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Solution Overview
Problem
Current methods for deriving 3D models of real-world environments are limited by the need for multiple cameras, restrictive perspectives, and computational challenges, especially when dealing with dynamic and deformable objects, leading to inaccuracies and the inability to provide real-time representations.
Innovation Solution
A method that processes image data to derive 2D and 3D descriptor data using neural networks and holonomic constraints, allowing for the construction of 3D skeletons and 4D spatio-temporal models that accurately represent environments and objects, enabling real-time manipulation and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras are used to capture environments from different perspectives, then measurement precision and reliability improve, but device complexity and cost increase significantly
Solution Approach 1:
The patent introduces an intermediary computational process that acts as a mediator between the single camera input and the 3D environment model output. By using neural networks and holonomic constraints as computational intermediaries, the system achieves multi-perspective 3D reconstruction without physically deploying multiple cameras, thus resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent creates virtual copies of multiple camera perspectives through computational rendering. Instead of using physical multiple cameras, the system generates synthetic multi-view images from a computed 3D environment model, achieving the observational benefits of multiple cameras while using only a single physical camera
2Manufacturing precision
If pixel-by-pixel processing is used to derive 3D models, then manufacturing precision improves, but productivity and processing speed deteriorate
Solution Approach 1:
The patent segments the image processing task from the 3D model construction task. Instead of processing pixels directly to build 3D models, the system first extracts semantic features and object descriptors as intermediate representations, then uses these segmented features to construct the 3D model. This segmentation reduces computational complexity while maintaining precision
Solution Approach 2:
The patent replaces the mechanical pixel-by-pixel processing approach with a neural network-based computational system. The neural network automatically learns and extracts relevant features from images, substituting the brute-force mechanical processing with an intelligent system that achieves both high precision and fast processing speeds
3Measurement precision
If traditional 3D modeling methods are used for deformable objects, then measurement precision deteriorates, but device complexity remains high
Solution Approach 1:
The patent applies dynamics by making the 3D model adaptable and flexible rather than rigid. The system uses deformable 3D models that can change shape and configuration to match the actual deformable objects being tracked. This dynamic modeling approach maintains measurement precision for deformable objects while the neural network automatically handles the complexity of tracking transformations
Solution Approach 2:
The patent changes the parameters used to describe deformable objects from fixed geometric parameters to flexible descriptor parameters that can adapt to shape changes. By using neural networks to learn and track object descriptors that evolve with deformation, the system maintains precision without requiring complex explicit modeling of each deformation mode
Data Source
AI summary
The present invention relates to a method for deriving a 3D data from image data comprising: receiving, from at least one camera, image data representing an environment; detecting, from the image data, at least one object within the environment; classifying the at least one detected object, wherein the method comprises, for each classified object of the classified at least one objects: determining a 2D skeleton of the classified object by implementing a neural network to identify features of the classified object in the image data corresponding to the classified object; and constructing a 3D skeleton for the classified object, comprising mapping the determined 2D skeleton to 3D.