ActMVS：基于单目多视图立体的主动场景重建
ActMVS: Active Scene Reconstruction with Monocular Multi-View Stereo
AI总结：
提出ActMVS框架，通过视图因子图构建和全局深度优化，实现单目相机在线生成高质量、全局一致的密集深度图，支持机器人/UAV的主动场景重建与安全轨迹规划。
摘要：
主动场景重建使机器人/UAV能够自主规划轨迹并重建环境，无需昂贵的手动数据采集。与被动方法不同，主动重建需要实时构建高置信度占据地图以实现无碰撞导航。现有方法依赖深度传感器更新占据地图，增加了平台成本和重量。为推进空间智能，我们旨在实现纯视觉单目解决方案。然而，当前单目场景重建方法离线运行，无法在机器人/UAV导航所需的帧率下提供全局一致的密集深度。为弥补这一差距，我们引入ActMVS，这是首个单目主动重建框架。我们的框架集成了用于信息多视图立体深度预测的视图因子图构建，以及全局深度优化，从而实现在线生成高质量、全局一致的密集深度图。这使得单目机器人/UAV能够在重建过程中维护可靠的占据地图，以实现安全的轨迹规划。在Replica数据集上的实验表明，其性能与RGB-D方法相当。我们的代码和数据可在https://github.com/TrickyGo/ActMVS获取。
英文摘要：
Active scene reconstruction enables robots/UAVs to autonomously plan trajectories and reconstruct environments without costly manual data acquisition. Unlike passive methods, active reconstruction requires real-time construction of high-confidence occupancy maps for collision-free navigation. Existing approaches rely on depth sensors for occupancy map updates, increasing platform cost and weight. To advance spatial intelligence, we aim for a vision-only monocular solution. However, current monocular scene reconstruction methods operate offline and fail to deliver globally consistent dense depth at the frame rates required for robots/UAVs navigation. To bridge this gap, we introduce ActMVS, the first framework for monocular active reconstruction. Our framework integrates a view factor graph construction for informed Multi-View Stereo depth prediction, along with a global depth optimization, to enable the online generation of high-quality, globally consistent dense depth maps. This enables monocular robots/UAVs to maintain reliable occupancy maps for safe trajectory planning during reconstruction. Experiments on Replica datasets demonstrate performance competitive with RGB-D methods. Our code and data are available at https://github.com/TrickyGo/ActMVS.
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。
