Monocular Camera Depth Estimation via Style Transfer Neural Networks
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Solution Overview
Problem
Monocular cameras capture 2D images without depth information, which is essential for autonomous, semi-autonomous, and active safety systems in vehicles.
Innovation Solution
A system that uses a monocular camera, processors, and a memory device with modules for image capture, style transfer, encoder-decoder, semantic information generation, and depth map generation, trained with a generative adversarial network to produce a synthesized image, feature map, and depth map, indicating estimated depth for vehicle navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a monocular camera is used to capture images, then the device complexity is reduced compared to using multiple cameras or LIDAR, but the depth information is lost
Solution Approach 1:
The patent introduces an intermediary processing system consisting of a neural network model with encoder-decoder architecture and style transfer module. This intermediary transforms the 2D monocular image into a depth map by learning the mapping between image features and depth information, effectively mediating between the limited 2D input and the required 3D depth output without adding physical sensors
Solution Approach 2:
The system changes the parameter representation from 2D image coordinates to 3D depth values by training a neural network to predict depth maps. The style transfer module further transforms the visual style of input images to enhance depth estimation accuracy under different lighting and weather conditions, effectively changing the parameter space to overcome the inherent limitations of monocular imaging
2Measurement precision
If style transfer and neural network processing are added to generate depth maps, then depth estimation accuracy is improved, but the processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary action by pre-training the neural network model offline using large datasets of paired images and depth maps. The style transfer module is also pre-trained to handle various lighting and weather conditions. This offline preparation enables the deployed system to perform real-time depth estimation with reduced processing time, as the complex learning processes have already been completed beforehand
Solution Approach 2:
The patent replaces traditional mechanical or optical depth measurement systems with a computational approach using neural networks and style transfer. This substitution allows for flexible, software-based depth estimation that can be optimized for different hardware platforms, balancing accuracy requirements with processing speed constraints through algorithmic efficiency improvements
Data Source
AI summary
A system for estimating depth using a monocular camera may include one or more processors, a monocular camera, and a memory device. The monocular camera and the memory device may be operably connected to the one or more processors. The memory device may include an image capture, an encoder-decoder module, a semantic information generating module, and a depth map generating module. The modules may configure the one or more processors to executed by one or more processors cause the one or more processors to obtain a captured image from the monocular camera, generate a synthesized image based on the captured image wherein the style transfer module was trained using a generative adversarial network, generate, a feature map based on the synthesized image, generate semantic information based on the feature map, and generate a depth map based on the feature map and the semantic information.