Neural 3D Road Surface Estimation Without LiDAR Complexity
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Solution Overview
Problem
Current 3D road surface reconstruction techniques for autonomous driving are inadequate due to the limitations of LiDAR sensors, such as high cost and limited range, and camera-based methods that struggle with accuracy and computational efficiency, particularly in complex urban environments.
Innovation Solution
The use of cameras to capture images of the 3D environment, combined with densification techniques like Markov random fields and deep neural networks, to generate a dense representation of the 3D road surface, enabling accurate and efficient reconstruction for autonomous vehicle navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR sensors are used for 3D road surface reconstruction, then measurement precision is improved, but device cost and complexity increase
Solution Approach 1:
The patent replaces LiDAR sensors (optical/mechanical system) with camera-based vision systems combined with neural network processing. The camera captures 2D images which are then processed through neural networks to reconstruct 3D road surface geometry, substituting direct optical ranging with computational imaging approaches.
Solution Approach 2:
The patent introduces neural networks as an intermediary processing layer between the camera sensor and the 3D reconstruction output. The neural network learns to map 2D image features to 3D road surface characteristics, acting as a mediator that translates visual information into geometric data without requiring direct depth measurement hardware.
2Measurement precision
If LiDAR sensors are used for 3D road surface reconstruction, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent employs standard consumer-grade cameras instead of expensive LiDAR sensors. Cameras are significantly cheaper, more widely available, and easier to manufacture into vehicles. The approach trades the high cost of specialized sensing hardware for computational resources that are becoming increasingly affordable.
3Ease of manufacture
If camera-based multi-view geometry is used for 3D reconstruction, then device cost is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent transforms the reconstruction approach from geometric parameter estimation (multi-view geometry) to learned parameter prediction (neural networks). Instead of relying on explicit camera calibration and triangulation mathematics, the neural network learns implicit geometric relationships from training data, achieving better precision for road surface specific tasks.
Solution Approach 2:
The patent applies specialized neural network architectures designed specifically for road surface reconstruction rather than general-purpose 3D reconstruction methods. The system focuses computational resources on extracting road-specific features and geometries, achieving higher local precision for the target application compared to generic multi-view geometry approaches.
4Device complexity
If conventional post-processing techniques are used for camera-based reconstruction, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent performs preliminary learning and feature extraction during the neural network training phase. The network is pre-trained on large datasets of road surfaces, learning to anticipate and correct for common reconstruction challenges before actual deployment. This preliminary action embeds domain knowledge that improves precision without adding runtime complexity.
Data Source
AI summary
In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated using a simulated environment. For example, a simulation may be run to simulate a virtual world or environment, render frames of virtual sensor data (e.g., images), and generate corresponding depth maps and segmentation masks (identifying a component of the simulated environment such as a road). To generate input training data, 3D structure estimation may be performed on a rendered frame to generate a representation of a 3D surface structure of the road. To generate corresponding ground truth training data, a corresponding depth map and segmentation mask may be used to generate a dense representation of the 3D surface structure.