3D Driving Scene Generation With Geometry-Guided Frame Interpolation
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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 leading to hallucinations and diffusion models lacking 3D constraints, making them unreliable for diverse and accurate scene simulations.
Innovation Solution
A framework that integrates geometry awareness by leveraging NeRF and diffusion models, using a key frame generation stage and interpolation stage to generate 3D-consistent scenes, incorporating geometric guidance to ensure consistency across depth and RGB videos, and employing a dual stream diffusion network to condition on text and HD maps.
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
Solution Approach 1:
The scene generation process is segmented into key frame generation and interpolation stages. Key frames are generated with explicit 3D geometry constraints, while interpolation fills unseen regions. This segmentation allows 3D consistency to be enforced where critical while using diffusion models to creatively fill gaps, reducing hallucinations.
Solution Approach 2:
Explicit 3D geometry information is prepared in advance as control signals before the diffusion process. Depth maps and 3D structures are generated first, then used to guide the diffusion model, ensuring that unseen regions are filled with geometrically consistent content rather than hallucinated elements.
2Adaptability or versatility
If diffusion models are used for scene generation, then diversity and creativity are improved, but geometric consistency deteriorates
Solution Approach 1:
Explicit 3D geometry information acts as an intermediary between the diffusion model and the final scene. The diffusion model generates diverse content while the 3D geometry control signals mediate to ensure geometric consistency, reconciling creativity with physical plausibility.
Solution Approach 2:
The diffusion model is conditioned on multiple parameters including text descriptions, HD maps, and explicit 3D geometry. By changing and combining these parameters, the system achieves both scene diversity through text and map variations and geometric consistency through 3D constraints.
3Measurement precision
If traditional NeRF methods are used, then 3D reconstruction accuracy is improved, but input data requirements increase
Solution Approach 1:
The method extracts and uses only the essential 3D geometry information as control signals, removing the need for complex input data such as multiple camera views and dense point clouds required by traditional NeRF. This extraction maintains reconstruction accuracy while simplifying input requirements.
4Stability of the object's composition
If geometry awareness is integrated, then scene consistency is improved, but computational complexity increases
Solution Approach 1:
The computational process is segmented into key frame generation with full 3D constraints and interpolation stages with reduced constraints. This segmentation maintains scene consistency in critical key frames while reducing computational complexity in the interpolation phases.
Solution Approach 2:
Full 3D geometric constraints are applied partially only to key frames rather than all frames. This partial action maintains scene consistency where most needed while reducing overall computational complexity during the interpolation process.
Data Source
AI summary
Systems and methods for generating a simulated scene include generating, by a first diffusion network, a first key frame based on a text description input and a high definition (HD) map input. The first key frame is warped to a second viewpoint. a second key frame is generated, by a second diffusion network, based on the text description input, the HD map input, and the warped first key frame. A middle frame is generated, by a third diffusion network, between the first key frame and the second key frame based on the text description input, the HD map input, and projections from the first key frame and the second key frame.