We introduce ActiveGAMER, an active mapping system that utilizes 3D Gaussian Splatting (3DGS) to achieve high-quality, real-time scene mapping and exploration. Unlike traditional NeRF-based methods, which are computationally demanding and restrict active mapping performance, our approach leverages the efficient rendering capabilities of 3DGS, allowing effective and efficient exploration in complex environments. The core of our system is a rendering-based information gain module that dynamically identifies the most informative viewpoints for next-best-view planning, enhancing both geometric and photometric reconstruction accuracy. ActiveGAMER 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 ActiveGAMER's effectiveness in active mapping tasks.

翻译：我们提出了ActiveGAMER，一种利用3D高斯泼溅（3DGS）实现高质量、实时场景建图与探索的主动建图系统。与计算需求高且限制主动建图性能的传统基于NeRF的方法不同，我们的方法利用3DGS的高效渲染能力，允许在复杂环境中进行有效且高效的探索。我们系统的核心是一个基于渲染的信息增益模块，它能动态识别出对下一最佳视角规划最具信息量的视点，从而提升几何与光度重建的精度。ActiveGAMER还集成了一个精心平衡的框架，结合了从粗到细的探索、后优化以及全局-局部关键帧选择策略，以最大化重建的完整性与保真度。我们的系统能够自主探索并重建环境，在几何与光度的精度和完整性方面均达到最先进水平，在这两方面显著超越了现有方法。在Replica和MP3D等基准数据集上的广泛评估突显了ActiveGAMER在主动建图任务中的有效性。