From Concept to Capability: Evaluating 3D Gaussian Splatting for Synthetic Scene Editing in Autonomous Driving

2026-05-03

AI Summary

Autonomous driving systems urgently require high-quality, diverse data of rare safety-critical scenarios, yet existing 3D Gaussian Splatting (3DGS) methods lack industrial-grade fidelity evaluation, limiting their applicability in safety-critical contexts. This work presents the first systematic evaluation framework tailored for autonomous driving, integrating multi-view camera and LiDAR data to quantitatively assess 3DGS performance in reconstructing key traffic participants such as vehicles and pedestrians. The study reveals degradation patterns in reconstruction quality under both lateral and longitudinal novel viewpoints, clarifying the method’s fidelity characteristics and strong viewpoint dependency. By addressing a critical gap in the validation of 3DGS for autonomous driving applications, this research provides essential insights to support its reliable integration into industrial development and testing pipelines.

Abstract

The perception of an Autonomous Driving System (ADS) critically depends on relevant, comprehensive, and diverse datasets to ensure its safety while operating in the environment. Field data collection lacks completeness with respect to the list of rare but still possible safety-related scenarios needed for the development, verification, and validation of the ADS. 3D Gaussian Splatting (3DGS) has shown promising capabilities for the reconstruction and editing of scenes based on data collected by cameras and LiDAR sensors. However, the industrial fidelity evaluation of reconstructions is underexplored, which is crucial when employing such methods in safety-related systems, especially for ADS. This becomes more challenging as ADS operates in a dynamic, uncontrolled environment with limited viewpoints and often partially occluded objects. This paper addresses this gap by proposing and implementing a framework (Fig. 1) to systematically analyze the capabilities and limitations of 3DGS for use in the reconstruction of safety-related scenes. It focuses on the quality of reconstruction for vehicles and pedestrians, which are the two most critical object classes for ADS. Our findings provide industry insights into the fidelity degradation of reconstructions from multiple novel viewpoints, both lateral and longitudinal, enabling the integration of these methods into real-world industrial AD software development and testing pipelines.

Problem

• Autonomous Driving
• 3D Gaussian Splatting
• Synthetic Scene Editing
• Fidelity Evaluation
• Safety-related Scenarios

Innovation

• 3D Gaussian Splatting
• autonomous driving
• scene reconstruction
• fidelity evaluation
• synthetic data