Open Map Data 3D Scene Generation for Reduced Domain Shift
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Solution Overview
Problem
Existing systems face challenges in generating large-scale, high-quality 3D scene simulations for computer vision tasks due to domain shift issues, lack of flexibility in scene simulation platforms, and inadequate utilization of target domain knowledge, leading to unsatisfactory data generation processes that hinder effective training and validation of computer vision models.
Innovation Solution
A system that uses open map data and random texture maps to automatically generate 3D urban scene layouts, incorporating target domain knowledge to reduce domain shift and improve generalization, by leveraging freely-available map information and texture maps from the same geographic locations, and employing stochastic processes to create synthetic datasets with detailed semantic labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually annotated real-world data is used for training, then data quality and accuracy are improved, but labor cost and time consumption increase significantly
Solution Approach 1:
The patent creates synthetic 3D scene data that copies the essential structural and semantic properties of real-world scenes without requiring manual annotation. By generating virtual scenes with automatic semantic labels from open map data, the system produces training data that replicates the information content of manually annotated data while eliminating the annotation process entirely.
Solution Approach 2:
The system enables self-service data generation by automatically creating synthetic training data with semantic annotations through stochastic scene generation processes. The open map data and rendering pipeline autonomously produce labeled datasets without human intervention, allowing the system to serve its own data annotation needs.
2Productivity
If arbitrary 3D scene states are used for simulation, then data generation speed is improved, but domain shift increases reducing model generalization
Solution Approach 1:
The patent systematically varies scene generation parameters such as camera positions, lighting conditions, object placements, and environmental features to create diverse synthetic scenes. By controlling and randomizing these parameters within realistic ranges, the system maintains high data generation speed while ensuring the synthesized data covers the variability needed for good model generalization.
Solution Approach 2:
The patent introduces open map data as an intermediary between arbitrary 3D scene generation and real-world target domains. The map data provides geographically-grounded structural information that mediates between completely synthetic scenes and real-world targets, reducing domain shift while maintaining generation efficiency.
3Manufacturing precision
If complex scene simulation platforms are used, then scene realism is improved, but system flexibility and accessibility decrease
Solution Approach 1:
The patent segments the scene generation system into independent modular components: open map data processing, stochastic scene generation, rendering engine, and annotation extraction. This segmentation allows each component to be selected, configured, and modified independently, providing flexibility while maintaining overall scene realism through coordinated operation of the modules.
Solution Approach 2:
The patent employs a universal rendering pipeline that can process various types of open map data from different sources and generate diverse scene types (urban, rural, indoor, outdoor). The system's multi-functionality allows it to adapt to different target domains and application requirements while maintaining a consistent core architecture that ensures scene realism.
Data Source
AI summary
Systems and methods for generating simulated scenes from open map data for machine learning are presented. The system includes an automatic scene generative pipeline that uses freely-available map information and random texture maps to create large-scale 3D urban scene layouts for supervised learning methods. The system generates synthetic datasets that have improved generalization capabilities with respect to a given target domain of interest using data from open maps and texture map from the same geographic locations. Data from the generation pipeline of the system improves a model's generalization to real image sets beyond arbitrarily-simulated sets or labeled real data from other geographical regions.