Neural Network Perspective Transformation via Depth Maps
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Solution Overview
Problem
Current computer vision techniques for autonomous vehicles struggle to generate realistic images from different perspectives, leading to distortion and poor recognition of objects, as they rely on synthetic data that lacks realism and does not accurately represent real-world scenarios.
Innovation Solution
A deep neural network employing an encoder-decoder architecture and conditional generative adversarial networks (cGANs) generates synthetic images by creating depth maps and point cloud representations, allowing for perspective transformation using camera pose matrices, thereby producing realistic images from varying viewpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional computer vision techniques are used for perspective transformation, then the process is simpler, but the generated images suffer from distortion and poor object recognition
Solution Approach 1:
The patent introduces depth maps and point cloud representations as intermediary data structures between the input image and the transformed output image. These intermediaries enable accurate 3D geometry reconstruction, which serves as a bridge to generate realistic perspective transformations without direct pixel manipulation, thereby resolving the contradiction between image quality and processing complexity.
Solution Approach 2:
The patent transitions from 2D image processing to 3D point cloud representation and back to 2D. By lifting the problem into three dimensions through depth estimation and point cloud generation, the system achieves accurate perspective transformations that preserve object geometry and realism, overcoming the limitations of traditional 2D transformation methods.
2Ease of manufacture
If synthetic data is used for training perception algorithms, then data acquisition is easier, but the data lacks realism and does not accurately represent real-world scenarios
Solution Approach 1:
The patent uses conditional generative adversarial networks (cGANs) to learn the mapping from real images to synthetic images while preserving realistic appearance characteristics. The network copies statistical properties and visual features from real-world data, enabling the generation of synthetic training data that maintains realism and accurately represents real-world scenarios while being easier to produce.
3Manufacturing precision
If deep neural networks with encoder-decoder architecture are used, then realistic images from different perspectives can be generated, but the computational complexity increases
Solution Approach 1:
The patent divides the complex image transformation task into multiple sequential stages: depth map generation, point cloud creation, point cloud transformation, and final image synthesis. Each stage is handled by specialized network components within the encoder-decoder architecture, allowing for modular optimization and reducing overall computational complexity while maintaining high image quality.
Data Source
AI summary
The present disclosure discloses a system and a method. In example implementations, the system and the method can include receiving an image having a first perspective; generating, via a deep neural network, a depth map corresponding to the image having the first perspective; generating, via the deep neural network, a point cloud representation based on the depth map; projecting the point cloud representation onto a point cloud representation corresponding to an image having a second perspective; generating a depth map corresponding to the image having the second perspective; and generating a synthetic image having the second perspective based on the depth map corresponding to the image having the second perspective and a semantic segmentation map corresponding to the image having the first perspective, wherein the second perspective is different from the first perspective.