用3D高斯点云实现高效实时场景探索，自动规划最佳观测视角。
ActiveGAMER: Active GAussian Mapping through Efficient Rendering
- 基于渲染信息增益动态选择最优观测视角，提升重建精度。
- 在Replica和MP3D数据集上达到当前最优几何与图像重建质量。
- 适合需要快速高精度环境建模的机器人或AR/VR应用。
我们提出ActiveGAMER，一个基于3D高斯溅射（3DGS）的主动映射系统，实现高质量、实时的场景建图与探索。相比传统基于NeRF的方法，该方法利用3DGS高效的渲染能力，在复杂环境中实现更有效的探索。系统核心是一个基于渲染的信息增益模块，可动态识别最具信息量的观测视角，用于下一步最佳视角规划，从而提升几何与光照重建的准确性。ActiveGAMER还整合了粗到精探索、后处理优化以及全局-局部关键帧选择策略，以最大化重建的完整性和保真度。系统能自主探索并重建环境，几何与光度重建精度和完整性均显著优于现有方法。在Replica和MP3D等基准数据集上的大量评估验证了其在主动建图任务中的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。