HGS-Planner: Hierarchical Planning Framework for Active Scene Reconstruction Using 3D Gaussian Splatting

1Fudan University
2Zhejiang University
3Shanghai Jiao Tong University

In a simulated complex house scene, we implemented our high-fidelity active reconstruction system on a mobile robot equipped with an RGB-D sensor. The colored curves represent the robot’s executed trajectories. We showcase the reconstruction results, which include the entire rendered scene, detailed renderings from three different views, and the variation in information gain at a specific view.

Abstract

In complex missions such as search and rescue,robots must make intelligent decisions in unknown environments, relying on their ability to perceive and understandtheir surroundings. High-quality and real-time reconstruction enhances situational awareness and is crucial for intelligent robotics. Traditional methods often struggle with poor scene representation or are too slow for real-time use. Inspired by the efficacy of 3D Gaussian Splatting (3DGS), we propose a hierarchical planning framework for fast and high-fidelity active reconstruction. Our method evaluates completion and quality gain to adaptively guide reconstruction, integrating global and local planning for efficiency. Experiments in simulated and real-world environments show our approach outperforms existing real-time methods.

Framework

System Architecture Pipeline of HGS-Planner.
An overview of our efficient autonomous reconstruction system with high-fidelity. Utilizing 3DGS for scene representation, the unobserved areas and the Fisher Information from the GS map are provided in real-time to evaluate the quality and completeness of the online reconstruction. Our proposed active reconstruction planning framework efficiently guides the robot to acquire new scene data, ensuring a comprehensive and high-fidelity 3DGS reconstruction.

Video

BibTeX

@article{xu2024hgs,
  title={Hgs-planner: Hierarchical planning framework for active scene reconstruction using 3d gaussian splatting},
  author={Xu, Zijun and Jin, Rui and Wu, Ke and Zhao, Yi and Zhang, Zhiwei and Zhao, Jieru and Gao, Fei and Gan, Zhongxue and Ding, Wenchao},
  journal={arXiv preprint arXiv:2409.17624},
  year={2024}
}