Synthetic 3D Point Cloud Generation for Autonomous Driving Training
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Solution Overview
Problem
Current methods for generating training datasets for deep neural networks operating on 3D point clouds are limited by the scarcity and quality of existing datasets, which are often based on unrealistic assumptions and require labor-intensive annotation, hindering the training of accurate neural networks for tasks like object detection and scene understanding.
Innovation Solution
A computer-implemented method generates realistic 3D point clouds by providing a 3D surface representation of a travelable environment, determining a traveling path, and generating virtual scans along this path, allowing for the creation of more accurate and diverse training datasets that mimic real-world scans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing public 3D point cloud datasets are used for training, then training data availability is improved, but the quantity and quality of training data are limited due to small dataset sizes and labor-intensive annotation processes
Solution Approach 1:
The patent creates synthetic 3D point cloud data by rendering virtual environments using 3D models and simulated sensor data, replacing the need for manual annotation of real-world scans. This copying approach generates unlimited training data without annotation time costs while maintaining realistic properties through physics-based rendering
Solution Approach 2:
The system automatically generates annotated 3D point cloud data by simulating sensor measurements and object detections in virtual environments, eliminating the need for human annotators. The self-service process produces both raw data and ground truth annotations simultaneously through automated rendering and simulation
2Productivity
If simple sampling schemes on mesh surfaces are used to generate artificial scans, then data generation speed is improved, but realism of the generated point clouds deteriorates due to unrealistic assumptions
Solution Approach 1:
The patent changes the generation parameters from simple uniform sampling to physics-based rendering parameters that simulate real sensor behavior, lighting conditions, and occlusion effects. This maintains high generation speed while improving realism by incorporating accurate physical models of how depth sensors actually capture data in real environments
Solution Approach 2:
The patent replaces manual or simple algorithmic sampling methods with automated physics-based rendering simulations. This substitution uses computational models of light transport, sensor geometry, and occlusion to generate realistic point clouds automatically, maintaining speed while dramatically improving realism
3Measurement precision
If more real-world scans are collected for training, then training data quality is improved, but the manual annotation process becomes more time-consuming and error-prone
Solution Approach 1:
Instead of collecting and manually annotating more real-world scans, the patent copies realistic properties through physics-based rendering of virtual environments. This approach maintains high measurement precision by simulating accurate sensor behavior and object properties without requiring complex manual annotation processes
Solution Approach 2:
The system automatically generates ground truth annotations for all training samples through simulated object detections and sensor measurements in virtual environments. This self-service annotation process eliminates human error and time consumption while maintaining or improving data quality through consistent, repeatable simulation parameters
Data Source
AI summary
A computer-implemented method for generating a training dataset. The training dataset includes training patterns each including a 3D point cloud of a respective travelable environment. The generating method includes, for each 3D point cloud, obtaining a 3D surface representation of the respective travelable environment, determining a traveling path inside the respective travelable environment, and, generating a virtual scan of the respective travelable environment along the traveling path, thereby obtaining the 3D point cloud. Such a method forms an improved solution for generating a training dataset of 3D point clouds.