Twin Variational Autoencoder for Vehicle Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing methods for training neural networks to detect objects and regions in images are inefficient due to the high cost, time, and resource consumption required for acquiring and labeling ground truth data, especially when used for autonomous vehicle operations.
Innovation Solution
A twin variational autoencoder (VAE) is trained using a combination of real and synthetic images, leveraging photorealistic image rendering software to generate simulated data that mimics real-world scenes, allowing for the generation of segmentation maps and depth maps without the need for extensive human-labeled training datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground truth data is acquired and labeled using traditional methods, then training accuracy is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent creates synthetic copies of real-world driving scenes using game engine simulations (Unity/Unreal). These synthetic images replicate real-world conditions including lighting, weather, and object appearances, providing unlimited training data without manual labeling. The synthetic dataset serves as a copy that preserves the essential characteristics of real data while eliminating the time-consuming labeling process.
Solution Approach 2:
The patent performs preliminary actions by pre-training the neural network on synthetic data before deploying it to real-world scenarios. This pre-training phase establishes a strong foundation that accelerates subsequent fine-tuning on real data, reducing the overall time required for model development and deployment.
2Reliability
If extensive human-labeled training datasets are used, then model performance is improved, but computational resources and expense increase
Solution Approach 1:
The patent replaces expensive, resource-intensive real labeled datasets with computationally efficient synthetic copies. These synthetic datasets provide sufficient training signal while requiring minimal computational resources to generate and process, dramatically reducing training costs and resource requirements.
Solution Approach 2:
The system uses automated scene generation and automatic labeling through simulation physics, eliminating the need for human annotators. The game engine automatically generates ground truth labels for all training samples, making the data preparation process self-service and resource-efficient.
3Measurement precision
If photorealistic synthetic images are generated using game engines, then data realism is improved, but generation complexity increases
Solution Approach 1:
The patent uses game engines (Unity/Unreal) that serve multiple functions: they generate photorealistic images, automatically create ground truth labels, simulate various weather and lighting conditions, and produce diverse driving scenarios all in a single unified platform. This multi-functionality reduces overall system complexity despite the advanced capabilities required.
Data Source
AI summary
A computer, including a processor and a memory, the memory including instructions to be executed by the processor to receive a monocular image and provide the image to a variational autoencoder neural network (VAE), wherein the VAE has been trained in a twin configuration that includes a first encoder-decoder pair that receives as input unlabeled real images and outputs reconstructed real images, and a second encoder-decoder pair that receives as input synthetic images and outputs reconstructed synthetic images and wherein the VAE includes third and fourth decoders that are trained using labeled synthetic images, segmentation ground truth and depth ground truth. The instructions can include further instructions to output from the VAE a segmentation map and a depth map based on inputting the monocular image.