CVPR2025

ActiveGAMER: Active GAussian Mapping through Efficient Rendering

Liyan Chen, Huangying Zhan, Kevin Chen, Xiangyu Xu, Qingan Yan, Changjiang Cai, Yi Xu

Abstract

We introduce ActiveGAMER, an active mapping system that utilizes 3D Gaussian Splatting (3DGS) to achieve highquality scene mapping and efficient exploration. Unlike recent NeRF-based methods, which are computationally demanding and limit mapping performance, our approach leverages the efficient rendering capabilities of 3DGS to enable effective and efficient exploration in complex environments. The core of our system is a rendering-based information gain module that identifies the most informative viewpoints for next-best-view planning, enhancing both geometric and photometric reconstruction accuracy. ActiveG-AMER also integrates a carefully balanced framework, combining coarse-to-fine exploration, post-refinement, and a global-local keyframe selection strategy to maximize reconstruction completeness and fidelity. Our system autonomously explores and reconstructs environments with state-of-the-art geometric and photometric accuracy and completeness, significantly surpassing existing approaches in both aspects. Extensive evaluations on benchmark datasets such as Replica and MP3D highlight ActiveG-AMER's effectiveness in active mapping tasks.