Abstract
We propose ActiveSplat, an autonomous high-fidelity reconstruction system leveraging Gaussian splatting. Taking advantage of efficient and realistic rendering, the system establishes a unified framework for online mapping, viewpoint selection, and path planning. The key to ActiveSplat is a hybrid map representation that integrates both dense information about the environment and a sparse abstraction of the workspace. Therefore, the system leverages sparse topology for efficient viewpoint sampling and path planning, while exploiting view-dependent dense prediction for viewpoint selection, facilitating efficient decision-making with promising accuracy and completeness. A hierarchical planning strategy based on the topological map is adopted to mitigate repetitive trajectories and improve local granularity given limited budgets, ensuring high-fidelity reconstruction with photorealistic view synthesis. Extensive experiments and ablation studies validate the efficacy of the proposed method in terms of reconstruction accuracy, data coverage, and exploration efficiency. Project page: this https URL.
Abstract (translated)
我们提出了一种名为ActiveSplat的自主高保真重建系统，该系统利用了高斯散射技术。凭借高效且逼真的渲染能力，该系统为在线地图构建、视点选择和路径规划建立了一个统一的框架。ActiveSplat的关键在于采用一种混合地图表示方法，既整合了环境中的密集信息，也包含了工作空间的稀疏抽象。因此，系统利用稀疏拓扑进行有效的视点采样和路径规划，并通过依赖于视角的密集预测来进行视点选择，从而在保证准确性和完整性的同时促进高效的决策制定。基于拓扑地图的分层规划策略被采纳以减少重复轨迹并提升局部细节，在预算有限的情况下确保了高保真度的重建与照片级真实的视图合成。广泛的实验和消融研究验证了所提出方法在重建精度、数据覆盖范围及探索效率方面的有效性。项目页面：此 https URL。
URL
https://arxiv.org/abs/2410.21955
