GauSS-MI：用于主动三维重建的高斯泼溅香农互信息
GauSS-MI: Gaussian Splatting Shannon Mutual Information for Active 3D Reconstruction
AI总结：
本文提出GauSS-MI准则，利用香农互信息实时量化每个高斯分布的视觉不确定性以选择下一最佳视角，并集成至主动重建系统，显著提升了三维重建的视觉质量与效率。
摘要：
本研究致力于解决实时主动视角选择与主动三维重建中视觉质量的不确定性量化挑战。视觉质量是三维重建的关键方面。NeRF与3DGS等近期进展显著提升了重建模型的图像渲染质量。然而，高效获取重建输入图像——即选择信息量最大的视角——仍是未解难题，而这对于主动重建至关重要。现有研究多聚焦于几何完整性评估与未知区域探索，未直接评估重建模型内部的视觉不确定性。为填补此空白，本文提出一种概率模型以量化每个高斯分布的视觉不确定性。利用香农互信息，我们构建了GauSS-MI准则，可实时评估新视角的视觉互信息，从而辅助选择下一最佳视角。GauSS-MI被集成至结合视角与运动规划器的主动重建系统中。在多种模拟与真实场景的实验表明，该系统在视觉质量与重建效率上表现卓越。
英文摘要：
This research tackles the challenge of real-time active view selection and uncertainty quantification on visual quality for active 3D reconstruction. Visual quality is a critical aspect of 3D reconstruction. Recent advancements such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have notably enhanced the image rendering quality of reconstruction models. Nonetheless, the efficient and effective acquisition of input images for reconstruction-specifically, the selection of the most informative viewpoint-remains an open challenge, which is crucial for active reconstruction. Existing studies have primarily focused on evaluating geometric completeness and exploring unobserved or unknown regions, without direct evaluation of the visual uncertainty within the reconstruction model. To address this gap, this paper introduces a probabilistic model that quantifies visual uncertainty for each Gaussian. Leveraging Shannon Mutual Information, we formulate a criterion, Gaussian Splatting Shannon Mutual Information (GauSS-MI), for real-time assessment of visual mutual information from novel viewpoints, facilitating the selection of next best view. GauSS-MI is implemented within an active reconstruction system integrated with a view and motion planner. Extensive experiments across various simulated and real-world scenes showcase the superior visual quality and reconstruction efficiency performance of the proposed system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
