Surfel Map Rendering for Scalable Simulated Sensor Data
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Solution Overview
Problem
Conventional methods for generating simulated environments for autonomous vehicle training are tedious and not scalable, requiring manual creation of realistic scenarios, which limits the amount of data that can be generated for training control systems.
Innovation Solution
A system using surfel maps and Generative Adversarial Networks (GANs) to generate realistic and detailed simulated sensor data, allowing for automatic creation of high-quality data for environments that have not been visited, enabling exploration of novel paths and reducing the need for real-world data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual creation of simulated environments is used, then the quality and realism of simulated environments is improved, but the productivity and scalability of data generation deteriorates
Solution Approach 1:
The patent uses real sensor data from actual environments to create surfel maps, which are then copied and rendered to generate synthetic training data. This copying approach maintains the realism and quality characteristics of real environments while enabling scalable automated generation of large datasets without manual recreation
Solution Approach 2:
The system transforms real-world sensor data into surfel representations by changing parameters such as point cloud density, surfel size, and orientation. These parameter transformations enable the conversion of complex real-world data into simplified yet realistic synthetic representations that can be efficiently generated and scaled
2Reliability
If more simulated data is generated to improve training quality, then the reliability of autonomous vehicle control systems is improved, but the loss of time and computational resources worsens
Solution Approach 1:
The patent pre-processes real sensor data into surfel maps and stores them in an efficient format. This preliminary action allows rapid generation of synthetic training data by simply rendering different views and scenarios from the pre-processed surfel representations, avoiding time-consuming manual or real-time data collection
Solution Approach 2:
The surfel map representation serves multiple functions: it can be used to generate training data for various autonomous driving scenarios, support different sensor types (camera, LiDAR), and enable both realistic environment simulation and novel path exploration from a single data source
3Measurement precision
If realistic detailed environments are created, then the measurement precision of sensor data is improved, but the device complexity increases
Solution Approach 1:
The patent replaces complex manual environment modeling with automated computational rendering of surfel maps. Instead of manually creating detailed 3D models, the system uses algorithmic processing of sensor data to generate realistic environments, reducing operational complexity while maintaining high measurement precision
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generated simulated sensor data. One of the methods includes obtaining a surfel map generated from sensor observations of a real-world environment and generating, for each surfel in the surfel map, a respective grid having a plurality of grid cells, wherein each grid has an orientation matching an orientation of a corresponding surfel, and wherein each grid cell within each grid is assigned a respective color value. For a simulated location within a simulated representation of the real-world environment, a textured surfel rendering is generated, including combining color information from grid cells visible from the simulated location within the simulated representation of the real-world environment.