Vehicle Gaussian Splatting with View-Conditioned Diffusion
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Solution Overview
Problem
The domain gap between synthetic and real-world data in autonomous driving datasets hinders effective training of autonomous vehicles, as synthetic data lacks semantic information and real-world data is costly and impractical to collect, leading to poor 3D reconstruction performance and inconsistency in generative diffusion models.
Innovation Solution
Transform single perspective images using image transformation techniques, fine-tune a pre-trained diffusion model with real-world data, generate perspective-aware images, and train a large generative model to create a gaussian splatting model for view-conditioned simulations, addressing the domain gap and enhancing 3D reconstruction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If synthetic data is used for training autonomous vehicles, then data collection cost is reduced, but semantic information and 3D reconstruction performance deteriorate
Solution Approach 1:
The patent creates a pipeline that copies real-world data characteristics into synthetic data through a multi-stage process: extracting 3D models and semantic information from real-world images, generating synthetic images that replicate real-world lighting and texture properties, and training diffusion models on these synthesized data. This allows synthetic data to inherit the semantic richness and photorealism of real-world data while maintaining the cost advantages of synthetic generation.
Solution Approach 2:
The patent transforms synthetic data by changing multiple parameters including lighting conditions, camera perspectives, and semantic annotations. The system generates synthetic images with varied lighting environments, applies depth information and semantic masks, and adjusts material properties to match real-world conditions. This parameter transformation enables synthetic data to bridge the domain gap between purely synthetic and real-world data.
2Measurement precision
If real-world data is collected for training autonomous vehicles, then semantic information and 3D reconstruction performance improve, but data collection cost and complexity increase
Solution Approach 1:
The patent introduces an intermediary system consisting of diffusion models and 3D reconstruction algorithms that mediate between real-world data and training datasets. The system captures real-world images, extracts 3D models and semantic information through the intermediary processing pipeline, and generates training data that preserves essential characteristics without requiring direct access to complex real-world scenarios. This intermediary layer simplifies data collection while maintaining data quality.
3Manufacturing precision
If domain gap between synthetic and real-world data is addressed, then 3D reconstruction accuracy improves, but training data generation complexity increases
Solution Approach 1:
The patent segments the data generation process into distinct modules: image transformation module for generating varied perspectives, diffusion model module for synthesizing realistic images, 3D reconstruction module for creating Gaussian splatting models, and training module for fine-tuning. This segmentation allows each module to be optimized independently and simplifies the overall complex task of bridging the domain gap while maintaining high 3D reconstruction accuracy.
Data Source
AI summary
Systems and methods for view-conditioned diffusion for real-world vehicle gaussian splatting. A single perspective image can be transformed using image transformation techniques to generate a training dataset that addresses a domain gap between synthetic data and real-world data in a traffic scene. A pre-trained diffusion model can be finetuned with the training dataset to obtain a fine-tuned diffusion model. Perspective-aware images having different perspective views of an entity from the single perspective image can be generated using the fine-tuned diffusion model. A large generative model (LGM) can be trained using the perspective-aware images to generate a gaussian splatting model for the entity. View-conditioned simulations from the single perspective image can be generated by using the gaussian splatting model for downstream tasks.