论文标题
从可穿戴视频中实现现场3D重建：V-Slam，NERF和视频图技术的评估
Towards Live 3D Reconstruction from Wearable Video: An Evaluation of V-SLAM, NeRF, and Videogrammetry Techniques
论文作者
论文摘要
混合现实（MR）是一项有望改变战争未来的关键技术。物理室外环境和虚拟军事训练的混合动力MR将使与真实和模拟的长途敌人交战。为了启用这项技术，必须根据实时传感器观察来维护物理环境的大规模3D模型。 3D重建算法应从高架和士兵级别的角度利用摄像机传感器的低成本和普遍性。可以平衡映射速度和3D质量，以实现动态环境中的实时MR培训。鉴于这些要求，我们调查了仅给出实时视频的军事应用的大规模映射的几种3D重建算法。我们从运动，视觉 - 峰和摄影测量技术中的共同结构中测量3D重建性能。这包括使用Instant-NGP的开源算法COLMAP，ORB-SLAM3和NERF。我们利用自动驾驶学术基准Kitti，其中包括仪表板相机视频和LIDAR产生3D地面真相。借助Kitti数据，我们的主要贡献是在考虑实时视频时对3D重建计算速度的定量评估。
Mixed reality (MR) is a key technology which promises to change the future of warfare. An MR hybrid of physical outdoor environments and virtual military training will enable engagements with long distance enemies, both real and simulated. To enable this technology, a large-scale 3D model of a physical environment must be maintained based on live sensor observations. 3D reconstruction algorithms should utilize the low cost and pervasiveness of video camera sensors, from both overhead and soldier-level perspectives. Mapping speed and 3D quality can be balanced to enable live MR training in dynamic environments. Given these requirements, we survey several 3D reconstruction algorithms for large-scale mapping for military applications given only live video. We measure 3D reconstruction performance from common structure from motion, visual-SLAM, and photogrammetry techniques. This includes the open source algorithms COLMAP, ORB-SLAM3, and NeRF using Instant-NGP. We utilize the autonomous driving academic benchmark KITTI, which includes both dashboard camera video and lidar produced 3D ground truth. With the KITTI data, our primary contribution is a quantitative evaluation of 3D reconstruction computational speed when considering live video.
