3D-Surface Reconstruction

3D 表面重建

• 批准号: 200549338
• 负责人: Professor Dr. Daniel Cremers
• 金额: --
• 依托单位: Lehrstuhl für Photogrammetrie
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2011
• 资助国家: 德国
• 起止时间: 2010-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/200549338?language=en
• 关键词: 3D; Surface; Reconstruction

The focus of P5 is the estimation of dense and high-resolution 3D reconstruction from the available image data. To this end, we pursue a two-stage strategy:(1) Based on the onboard stereo camera, we aim at computing a crude semi-dense visual SLAM (depth maps, geometry and camera motion). It is computed at video frame rate on the quadrotor and takes into account the statistical uncertainty of 3D locations and image information.(2) On the ground station, we improve the semi-dense 3D reconstruction toward a high-resolution dense 3D reconstruction by also taking into account the high-resolution images from the main sensor. More specifically we plan to generate dense depth maps which are then fused to a dense 3D model. This step will also take into account 3D point clouds from bundle adjustment and 3D point clouds from the laser scanner. In this second phase of the project, we will emphasize robustness to perturbations through reflections, homogeneous surfaces and over-exposure.A central advantage of this two-stage strategy is that we will have a rudimentary self-localization and semi-dense 3D reconstruction of the scene computed onboard in a manner that does not depend on the communication with the ground station.The resulting semi-dense 3D reconstruction can for example be used for an adaptive viewpoint planning: During the flight, the system can immediately identify for which locations it has not obtained sufficient visual information yet. This information can in turn be used to adapt the flight trajectory so as to assure an optimal exploration of unknown territory.

P5的重点是从现有的图像数据中估计密集和高分辨率的3D重建。为此，我们采取了两个阶段的策略：(1)基于车载立体摄像机，我们的目标是计算一个粗略的半密集视觉SLAM(深度图、几何和摄像机运动)。在地面站上，我们将半密集三维重建改进为高分辨率的密集三维重建，同时也考虑了主传感器的高分辨率图像。更具体地说，我们计划生成密集的深度图，然后将其融合到密集的3D模型中。此步骤还将考虑来自束平差的3D点云和来自激光扫描仪的3D点云。在这个项目的第二阶段，我们将强调对反射、均匀表面和过度曝光的扰动的稳健性。这种两阶段策略的一个主要优势是，我们将在飞机上对场景进行基本的自定位和半密集3D重建，计算方式不依赖于与地面站的通信。产生的半密集3D重建可以用于自适应视点规划：在飞行过程中，系统可以立即识别它尚未获得足够视觉信息的位置。这些信息可以反过来用来调整飞行轨迹，以确保对未知领域的最佳探索。

项目成果

Real-time trajectory replanning for MAVs using uniform B-splines and a 3D circular buffer

• DOI: 10.1109/iros.2017.8202160
• 发表时间: 2017-03
• 期刊: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: V. Usenko;L. Stumberg;A. Pangercic;D. Cremers