Learning-Based 3D Property Extraction from Live 2D Images
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Solution Overview
Problem
Existing methods for determining three-dimensional properties of objects during events, such as sports matches, rely on calibrated two-dimensional cameras, which can be cumbersome and inefficient, especially for real-time applications.
Innovation Solution
A learning-based 3D property extractor that captures live 2D images of events using a camera and employs a neural network trained with 2D and 3D data from prior events to recognize 3D properties like location, size, and velocity of objects, enabling accurate and efficient 3D property extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple calibrated 2D cameras are deployed to determine 3D properties, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces the mechanical calibration system (multiple calibrated cameras requiring precise geometric relationships) with a neural network-based learning system. The neural network is trained on 2D images paired with ground truth 3D annotations, enabling it to directly infer 3D properties from single 2D images without requiring complex camera calibration or multiple synchronized cameras.
Solution Approach 2:
The patent uses a neural network that has been trained on a dataset of 2D images with corresponding 3D annotations. During inference, the network copies the learned mapping from training data to new images, enabling 3D property extraction without requiring the actual physical calibration setup that was used during training.
2Measurement precision
If multiple calibrated 2D cameras are deployed for real-time 3D property determination, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent performs preliminary training of the neural network offline using a large dataset of 2D images with ground truth 3D annotations. This preliminary action creates a pre-trained model that can rapidly infer 3D properties during real-time operation, separating the computationally intensive learning phase from the efficient inference phase.
Solution Approach 2:
The patent replaces the real-time computational burden of multi-camera triangulation and calibration with a pre-trained neural network that performs 3D property inference through learned patterns, significantly improving processing speed for real-time applications.
3Reliability
If multiple calibrated 2D cameras are deployed, then reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The patent replaces the complex mechanical calibration process required for reliable multi-camera systems with a neural network that has been trained on diverse data. The network handles the complexity of 3D inference internally, providing reliable results through learned robustness to variations in lighting, pose, and occlusion without requiring user-performed calibration.
Data Source
AI summary
Learning-based 3D property extraction can include: capturing a series of live 2D images of a participatory event including at least a portion of at least one reference visual feature of the participatory event and at least a portion of at least one object involved in the participatory event; and training a neural network to recognize at least one 3D property pertaining to the object in response to the live 2D images based on a set of timestamped 2D training images and 3D measurements of the object obtained during at least one prior training event for the neural network.