发表机构
The University of Hong Kong（香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于2D高斯泼溅的主动重建框架，通过概率可靠性模型和互信息度量平衡视觉与几何质量，在八个Replica场景上以更低计算成本实现高效重建。
AI 中文摘要
主动重建需要在有限的车载计算资源内进行高效的主动视角选择，以实现高质量的重建。现有方法在充分平衡视觉与几何质量以及实时操作所需的计算效率方面面临挑战。在这项工作中，我们提出了一种基于2D高斯泼溅（2DGS）的主动重建框架。我们开发了一个高效的在线2DGS建图流程，用于增量式RGB-D观测，并引入了一个概率可靠性模型，该模型表征了单个2D高斯泼溅的视角相关重建质量。基于该模型，我们提出了2D高斯泼溅香农互信息（2D GauSS-MI），这是一种基于互信息的度量，利用2DGS的显式表面方向来评估候选视角的预期信息增益。所提出的度量使主动视角选择能够同时考虑视觉和几何重建质量。我们在八个Replica场景上对所提出的系统与三个最先进的基线进行了评估。实验结果表明，我们的方法在视觉和几何重建质量之间取得了良好的平衡，同时具有显著更低的计算成本和具有竞争力的模型存储。
英文摘要
Active reconstruction requires efficient active view selection to achieve high-quality reconstruction within limited onboard computational resources. Existing methods face challenges in adequately balancing visual and geometric quality with the computational efficiency required for real-time operation. In this work, we present an active reconstruction framework based on 2D Gaussian Splatting (2DGS). We develop an efficient online 2DGS mapping pipeline for incremental RGB-D observations and introduce a probabilistic reliability model that characterizes the view-dependent reconstruction quality of individual 2D Gaussian splats. Building on this model, we formulate 2D Gaussian Splatting Shannon Mutual Information (2D GauSS-MI), a mutual-information-based metric that exploits the explicit surface orientation of 2DGS to evaluate the expected information gain of candidate views. The proposed metric enables active view selection to account for both visual and geometric reconstruction quality. We evaluate the proposed system against three state-of-the-art baselines on eight Replica scenes. Experimental results demonstrate that our method achieves a favorable balance between visual and geometric reconstruction quality with substantially lower computational cost and competitive model storage.
