3D Scene Generation With Geometry-Guided Diffusion Consistency
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Solution Overview
Problem
Existing 3D scene generation methods for autonomous driving simulations face challenges in achieving geometric consistency and physical plausibility, with NeRF-based methods suffering from hallucinations and diffusion models lacking 3D constraints, leading to inconsistent and unreliable scene generation.
Innovation Solution
A framework that integrates geometry consistency guidance into the scene generation process by leveraging both NeRF and diffusion models, using depth and RGB videos to enforce geometric consistency, and incorporating geometric priors through key frame generation and interpolation stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If NeRF-based methods are used for 3D scene generation, then 3D consistency is improved, but hallucinations in unseen regions occur and input data requirements become strict
Solution Approach 1:
The patent merges NeRF-based methods with diffusion models to combine the 3D consistency strengths of NeRF with the generative capabilities of diffusion models. The system uses NeRF to generate consistent 3D representations while employing diffusion models to fill unseen regions, thereby reducing hallucinations and relaxing input data requirements.
Solution Approach 2:
The patent introduces an intermediary process that uses depth estimation and geometric consistency guidance to bridge between the NeRF-based 3D reconstruction and diffusion-based generation. This intermediary ensures that generated content maintains geometric consistency with the input data while allowing creative filling of unseen regions.
2Ease of manufacture
If diffusion models are used for scene generation, then photorealistic images can be generated with relaxed input requirements, but geometric consistency and physical plausibility deteriorate
Solution Approach 1:
The patent introduces geometric consistency guidance as an intermediary mechanism that constrains the diffusion model's generation process. This guidance ensures that generated frames maintain geometric consistency with the input data and each other, preventing physically implausible content while preserving the ease of use benefits of diffusion models.
Solution Approach 2:
The patent implements feedback mechanisms where generated depth information and geometric constraints are fed back into the diffusion process to guide subsequent generation steps. This feedback loop ensures that geometric consistency is maintained throughout the generation process while allowing photorealistic image synthesis.
3Measurement precision
If traditional NeRF methods are used, then 3D reconstruction accuracy is improved, but the complexity of input data requirements increases
Solution Approach 1:
The patent extracts and relaxes the strict input data requirements of traditional NeRF methods by selectively using only essential inputs (such as a single image or video sequence) while deriving other necessary information (depth, geometry) through estimation and generation processes. This reduces the complexity of data collection while maintaining 3D reconstruction accuracy.
Data Source
AI summary
Systems and methods for generating a three-dimensional (3D) scene include generating a depth video based on a text description input, a high-definition (HD) map input, and an ego trajectory input wherein geometry consistency guidance is applied to enforce geometry consistency in the depth video. A color video is generated based on the text description input, the HD map input, the ego trajectory input, and the depth video wherein geometry consistency guidance is applied to enforce geometry consistency in the color video; and generating a 3D scene based on the depth video, the color video, and the ego trajectory input.