DrivingGaussian: Composite Gaussian Splatting for Dynamic Autonomous Driving Scenes

核心概念

DrivingGaussian introduces Composite Gaussian Splatting for high-quality dynamic scene reconstruction in autonomous driving.

摘要

• Introduction:
  • Representing large-scale dynamic scenes is crucial for autonomous driving tasks.
  • Challenges arise due to high-speed movements of ego vehicles and dynamic objects.
• Neural Radiance Fields:
  • NeRF-based methods struggle with unbounded scenes and multi-camera setups at high speeds.
• 3D Gaussian Splatting:
  • Represents scenes with explicit 3D Gaussians but faces challenges in modeling dynamic driving scenes.
• Composite Gaussian Splatting:
  • Hierarchically models complex driving scenes using static background and dynamic objects separately.
• LiDAR Prior:
  • Enhances geometric accuracy and multi-view consistency in scene reconstruction.
• Global Rendering:
  • Utilizes differentiable 3D Gaussian splatting renderer for global rendering of composite Gaussians.
• Experiments:
  • Outperforms existing methods on nuScenes and KITTI-360 datasets, enabling corner case simulation.

統計資料

DrivingGaussian achieves state-of-the-art performance on the nuScenes dataset. (PSNR: 28.74, SSIM: 0.865)
LiDAR prior improves geometric accuracy in scene reconstruction.

引述

從以下內容提煉的關鍵洞見

DrivingGaussian

by Xiaoyu Zhou,... 於 arxiv.org 03-21-2024

https://arxiv.org/pdf/2312.07920.pdf

深入探究

How can the use of LiDAR prior impact the scalability of the DrivingGaussian framework

LiDAR prior can have a significant impact on the scalability of the DrivingGaussian framework by providing more accurate geometric structures and maintaining multi-view consistency. By leveraging LiDAR data as an initialization for Gaussians, the framework can better capture precise geometries in large-scale scenes. This not only enhances the quality of scene reconstruction but also improves the efficiency of modeling dynamic objects within driving scenarios. The use of LiDAR prior allows for more accurate representation of complex scenes, leading to better performance in surround-view synthesis and dynamic scene reconstruction.

What are the potential limitations of Composite Gaussian Splatting in handling highly dynamic driving scenarios

Composite Gaussian Splatting may face limitations when handling highly dynamic driving scenarios due to several factors. One limitation is related to fast-moving objects that undergo significant spatial changes, making it challenging to represent them accurately using fixed Gaussians. In such scenarios, there could be issues with occlusion between multiple dynamic objects and difficulties in maintaining temporal coherence across frames. Additionally, capturing detailed textures and fine details in highly dynamic scenes might pose challenges for Composite Gaussian Splatting's representation capacity.

How might the principles of DrivingGaussian be applied to other fields beyond autonomous driving

The principles underlying DrivingGaussian can be applied beyond autonomous driving to various other fields that require complex scene understanding and reconstruction. For example:

Augmented Reality (AR) and Virtual Reality (VR): DrivingGaussian's hierarchical modeling approach could be utilized for creating realistic virtual environments with interactive elements.
Robotics: The framework's ability to handle static backgrounds and multiple moving objects could benefit robot navigation systems operating in dynamic environments.
Medical Imaging: DrivingGaussian's techniques could aid in reconstructing 3D models from medical imaging data like MRI or CT scans, enhancing visualization accuracy.
Video Games: Implementing similar methodologies could improve rendering capabilities for immersive gaming experiences with lifelike scenes.
By adapting the concepts of incremental static background modeling and composite dynamic object representation, these fields can leverage advanced scene reconstruction techniques for enhanced applications beyond autonomous driving contexts.