Simulation Scene Image Generation From White 3D Models Using GANs
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Solution Overview
Problem
The process of building simulation scenes for smart driving and robotics is tedious, time-consuming, and inefficient, requiring significant manpower and resources, with poor scalability and high hardware/software demands for rendering.
Innovation Solution
A simulation scene image generation method that utilizes a white blank 3D environment model, semantic and instance segmentation information, and a pre-trained generative adversarial network to automatically generate simulation scenes, allowing for editable instance attributes and reducing the need for manual refinement of attributes like color, texture, and illumination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual scene mapping and model building is used, then scene detail accuracy is improved, but construction time and resource consumption increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of scene mapping and model building with an automated system using generative adversarial networks (GANs). The GAN takes sparse input data and automatically generates complete simulation scene images, eliminating the need for manual refinement of colors, textures, and lighting while significantly reducing construction time.
Solution Approach 2:
The patent uses generative adversarial networks to create synthetic copies of real-world scenes. Instead of manually reconstructing scenes, the system learns from real scene data and generates realistic simulation scene images that replicate the appearance and characteristics of actual environments, achieving both accuracy and efficiency.
2Manufacturing precision
If manual model building with detailed refinement is performed, then scene realism is improved, but scalability deteriorates
Solution Approach 1:
The patent creates a universal GAN-based system that can generate diverse simulation scenes from different domains (smart driving, robots, etc.) using the same underlying technology. The system handles various scene types, objects, and conditions through a single automated framework, enabling easy scaling across multiple applications without requiring separate manual building processes for each scene.
Solution Approach 2:
The patent enables scalable scene generation by changing input parameters such as scene configuration data, object types, and environmental conditions. The GAN system responds to parameter changes by automatically adjusting generated scene characteristics, allowing rapid creation of diverse scenes without manual intervention and improving adaptability across different application scenarios.
3Manufacturing precision
If high-quality simulation scenes are generated manually, then image quality is improved, but hardware and software resource requirements increase
Solution Approach 1:
The patent replaces complex manual scene building processes with a learned GAN system that automatically generates high-quality simulation scenes. The GAN learns efficient representations of scene data during training and can generate realistic images with appropriate hardware resources, reducing the need for excessive computational power while maintaining image quality.
4Productivity
If automated generation methods are used, then construction efficiency is improved, but scene diversity may deteriorate
Solution Approach 1:
The patent maintains scene diversity through parameter changes by allowing flexible input configurations for the GAN system. Different scene types, objects, lighting conditions, and environmental parameters can be specified as inputs, and the GAN generates corresponding diverse scenes automatically. This enables high efficiency while preserving adaptability across various smart driving and robotics scenarios.
Data Source
AI summary
A simulation scene image generation method, an electronic device and a storage medium are provided. The method includes: acquiring semantic segmentation information and instance segmentation information of a white blank 3D environment model; receiving instance text information of the white blank 3D environment model, the instance text information being editable information and used for describing an instance attribute; and generating a simulation scene image based on the semantic segmentation information, the instance segmentation information, the instance text information and a pre-trained generative adversarial network. In the method, only the establishment of the white blank 3D environment model is required, so that the simulation scene image can be generated based on the semantic segmentation information and the instance segmentation information of the white blank 3D environment model, and attributes such as color, texture and illumination do not need to be refined during establishment of the scene, thereby improving a generation efficiency.