Agent NeRF Synthesis for Photorealistic Traffic Simulation
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Solution Overview
Problem
Traditional methods for creating and editing 3D assets for robust simulation systems in self-driving and ADAS are labor-intensive, costly, and not scalable, making it difficult to generate diverse and photorealistic traffic scenarios.
Innovation Solution
The method involves extracting agent neural radiance fields (NeRFs) from driving video logs, storing them in a database, and editing traffic scenes by inserting, replacing, or removing agents based on photorealism criteria, with the edited agent NeRFs composed with scene NeRFs for volume rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional manual 3D asset creation methods are used, then photorealism and control quality are improved, but labor intensity and cost increase significantly
Solution Approach 1:
The patent extracts agent NeRFs from real driving video logs to create a database of pre-computed 3D representations. These copied agent models can be directly inserted into simulated traffic scenes, eliminating the need for manual 3D asset creation while maintaining photorealism. The system copies real-world agents into the simulation environment through NeRF transfer.
Solution Approach 2:
The patent replaces manual mechanical 3D asset creation processes with automated neural network-based extraction. Instead of manually modeling agents, the system uses NeRF extraction algorithms to automatically generate 3D representations from video data, substituting human labor with computational processes.
2Adaptability or versatility
If traditional procedural rendering pipelines are used, then scene generation capability is improved, but scalability and cost-effectiveness deteriorate
Solution Approach 1:
The patent segments the traffic scene into distinct components: scene NeRF representing the environment and agent NeRFs representing individual traffic participants. This segmentation allows independent extraction, storage, and manipulation of each component, enabling scalable generation of diverse traffic scenarios by simply combining pre-extracted agents with scene representations.
Solution Approach 2:
The agent NeRF database serves multiple functions: it stores extracted agent representations, provides photorealistic agents for scene insertion, and enables diverse scenario generation. This universal approach allows a single database to support various simulation needs without requiring separate asset creation pipelines for each scenario type.
3Manufacturing precision
If manual 3D asset creation is used, then asset quality and control are improved, but time consumption and cost increase
Solution Approach 1:
The system performs preliminary extraction of agent NeRFs from real driving video logs and stores them in a database before they are needed for simulation. This preliminary action eliminates the time-consuming manual creation process during actual scene generation, as agents can be directly retrieved and inserted from the pre-computed database.
Data Source
AI summary
A computer-implemented method for synthesizing an image includes extracting agent neural radiance fields (NeRFs) from driving video logs and storing agent NeRFs in a database. For a driving video log to be edited, a scene NeRF and agent NeRFs are extracted from the driving video log to be edited. One or more agent NeRFs are selected from the database to insert into or replace existing agents in a traffic scene of the driving video log based on photorealism criteria. The traffic scene is edited by inserting a selected agent NeRF into the traffic scene, replacing existing agents in the traffic scene with the selected agent NeRF, or removing one or more existing agents from the traffic scene. An image of the edited traffic scene is synthesized by composing edited agent NeRFs with the scene NeRF and performing volume rendering.