Synthesizing 3D Data with GANs for Autonomous Vehicle Navigation
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Solution Overview
Problem
Current methods for generating 3D data for autonomous vehicle navigation face challenges such as high computational complexity, voluminous data, and integration issues between LiDAR and camera data, which complicates object detection and increases latency.
Innovation Solution
A method and system using generative adversarial networks (GANs) to synthesize 3D data by clustering initial data to identify regions of interest, generating noise data with a Gaussian mixture model, and iteratively transforming the data to create affine transformations, reducing data volume and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LiDAR and camera data are fused to generate 3D data for object detection, then data volume increases and computational complexity increases, but object detection capability improves
Solution Approach 1:
The patent uses generative adversarial networks (GANs) to synthesize 3D data from 2D images, creating virtual copies of 3D point clouds. This allows the system to generate training data without actually acquiring and processing voluminous LiDAR data, thereby reducing computational complexity while maintaining object detection capability through the synthesized 3D representations
Solution Approach 2:
The patent extracts essential 3D structural information from 2D images using GANs, separating the critical geometric data from the unnecessary voluminous LiDAR data. This extraction process enables the system to work with reduced-dimensional data that captures object essence without the computational burden of full 3D point cloud processing
2Measurement precision
If multiple 2D images are used to reconstruct 3D data, then depth information improves, but data volume remains high and computational complexity increases
Solution Approach 1:
The patent generates synthetic 3D data copies from 2D images using GANs, creating compact representations that preserve depth information without requiring the processing of multiple original 2D images. This copying approach reduces data volume while maintaining the essential depth measurements needed for 3D reconstruction
Solution Approach 2:
The patent transforms 2D image data into 3D point cloud representations through GAN-based parameter transformation, changing the data representation from pixel coordinates to spatial coordinates. This parameter change enables compact storage and processing of 3D information while preserving depth precision
3Measurement precision
If LiDAR data is used for object detection, then 3D spatial information improves, but data acquisition cost and volume increase
Solution Approach 1:
The patent creates virtual copies of LiDAR 3D data by generating synthetic point clouds from 2D images. These copies provide the necessary 3D spatial information without requiring actual LiDAR hardware or processing voluminous LiDAR point clouds, thereby reducing data acquisition cost and volume while maintaining spatial precision
Solution Approach 2:
The patent replaces the mechanical LiDAR scanning process with a computational approach using GANs to generate 3D data from 2D images. This substitution eliminates the need for physical LiDAR hardware and reduces data volume by generating only the necessary 3D representations rather than capturing complete spatial scans
Data Source
AI summary
This disclosure relates generally to generating synthetic data, and more particularly to method and system for synthesizing three-dimensional (3D) data using generative adversarial networks (GANs). The method may include clustering initial 3D data to identify one or more regions of interest (ROIs), generating an input specific noise data based on the one or more ROIs by an iterative process using Gaussian mixture model, and iteratively synthesizing the 3D data based on the one or more ROIs and the input specific noise data using generative adversarial networks (GANs) to generate final synthesized 3D data. The initial 3D data may represent a given scenario, while the final synthesized 3D data may represent a number of possible scenarios and are affine transforms of the initial 3D data.