Geometry-Aware Driving Scene Generation With 3D Consistency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 3D scene generation methods for autonomous driving simulations face challenges in achieving geometric consistency and physical feasibility, with NeRF-based methods leading to hallucinations and diffusion models lacking 3D constraints, resulting in unreliable and inconsistent scene generation.
Innovation Solution
A framework that integrates geometry-aware guidance into the scene generation process by leveraging both NeRF and diffusion models, using depth and RGB videos to enforce geometric consistency, and incorporating geometry 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 to reconstruct driving video into 3D volume representation, then 3D consistency is improved, but hallucinations in unseen regions occur and input data requirements become strict
Solution Approach 1:
The generation process is segmented into key frame generation and intermediate frame generation stages. Key frames are generated with explicit 3D geometry constraints to ensure accuracy, while intermediate frames are generated with relaxed constraints to improve efficiency. This segmentation allows different parts of the video generation process to have different levels of geometric constraint, reducing hallucinations in critical regions while maintaining overall 3D consistency.
Solution Approach 2:
Explicit depth maps and 3D geometry representations are introduced as intermediary elements between the input video and the generated 3D scene. These intermediaries serve as geometric priors that guide the NeRF reconstruction process, ensuring that the 3D volume representation maintains geometric consistency without requiring strict input data formats or causing hallucinations in unseen regions.
2Illumination intensity
If diffusion models are used for driving scene simulations, then photorealistic image generation is improved, but geometric consistency and physical feasibility deteriorate
Solution Approach 1:
The patent merges the strengths of diffusion models (photorealistic image generation) with the geometric constraints of NeRF-based methods. The combined approach uses diffusion models to generate high-quality images while simultaneously enforcing 3D geometric consistency through explicit depth maps and volume representation constraints, ensuring both photorealism and geometric accuracy in the generated scenes.
Solution Approach 2:
The patent changes the parameter space by introducing explicit geometric parameters (depth maps, 3D bounding boxes, camera poses) alongside the standard image generation parameters. This allows the system to control both the visual quality (through diffusion model parameters) and the geometric consistency (through 3D constraint parameters) simultaneously, resolving the contradiction between photorealism and geometric accuracy.
3Stability of the object's composition
If traditional NeRF methods are used for driving scene reconstruction, then 3D volume representation is improved, but input data requirements become strict and diversity of simulations is limited
Solution Approach 1:
The patent performs preliminary extraction of geometric priors (depth maps, 3D object boundaries, camera poses) from input videos before the main reconstruction process. These pre-extracted geometric constraints are then used to guide the NeRF reconstruction, allowing the system to maintain accurate 3D volume representation while being more adaptable to different input formats and simulation scenarios, thus improving both 3D accuracy and simulation diversity.
Data Source
AI summary
Systems and methods for generating 3D scenes include a masked red, green, blue, depth (RGBD) input, which is separated into a masked RGB input and a masked depth input. The masked depth input is compressed. The masked RGB input is compressed. A high definition (HD) map control signal is generated for a depth stream, and an HD map control signal is generated for an RGB stream. A depth output is generated based on inputs from the depth stream, the HD map control signal for the depth stream, text encoder, and random sampled noise. An RGB output is generated based on inputs from the RGB stream, the HD map control signal for an RGB stream, text encoder, and random sampled noise to train a dual stream diffusion network.