Neural Network Orientation Prediction Without Ground Truth Labels
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Solution Overview
Problem
Training neural networks to predict object orientations in images requires significant memory, time, and computing resources, especially when ground truth annotations are unavailable or difficult to obtain.
Innovation Solution
Training neural networks in a self-supervised manner using a collection of images without ground truth annotations, employing loss functions such as generative consistency, symmetry, nearest neighbor, and farthest neighbor losses to infer object orientations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained using ground truth annotations, then prediction accuracy is improved, but memory requirements and computing resources increase significantly
Solution Approach 1:
The patent creates synthetic images that copy the structural and orientational characteristics of real images without requiring actual ground truth annotations. The neural network learns from these synthetic copies, which contain encoded orientation information derived from the original images through geometric transformations and rendering, thereby achieving accurate training without the memory burden of storing annotated ground truth data
Solution Approach 2:
The patent introduces a 3D model renderer and synthetic image generator as intermediary components between the input images and the neural network training process. These intermediaries transform real images into synthetic images that contain orientation information in an encoded format, allowing the network to learn orientations without direct access to ground truth annotations, thus reducing memory requirements
2Reliability
If ground truth annotations are used for training, then model performance is improved, but training time and computing resources increase
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing 3D models of objects along with their ground truth orientations in a database before the actual training process. During training, the system retrieves relevant 3D models and generates synthetic images from pre-stored models rather than processing actual annotated images, significantly reducing training time while maintaining performance
Solution Approach 2:
The patent creates synthetic images that copy the essential characteristics of real images including orientation information, allowing the neural network to train efficiently without processing time-consuming ground truth annotation processes. The synthetic images are generated by rendering 3D models with known orientations, providing ready-to-use training data that reduces computational time
3Measurement precision
If ground truth annotations are required, then prediction accuracy is improved, but ease of data collection deteriorates
Solution Approach 1:
The patent creates synthetic images that copy the visual characteristics and orientation information from real images without requiring manual annotation. The system generates these synthetic copies by rendering 3D models with known orientations, automatically producing training data that would otherwise require time-consuming human annotation processes
Solution Approach 2:
The system performs self-service by automatically generating its own training data through 3D model rendering and synthetic image creation. Instead of relying on external annotation processes, the system uses its own 3D model database to generate training images with embedded orientation information, making the data collection process autonomous and efficient
Data Source
AI summary
Apparatuses, systems, and techniques to identify orientations of objects within images. In at least one embodiment, one or more neural networks are trained to identify an orientations of one or more objects based, at least in part, on one or more characteristics of the object other than the object's orientation.