图 书 馆
基于半稠密场景图像的刚体流估计Dense Semi-rigid Scene Flow Estimation from RGBD Images |
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| 课程网址: | http://videolectures.net/eccv2014_quiroga_flow_estimation/ |
| 主讲教师: | Julián Quiroga |
| 开课单位: | 法国国家信息与自动化研究所 |
| 开课时间: | 2014-10-29 |
| 课程语种: | 英语 |
| 中文简介: |  场景流被定义为3D空间中的运动场，并且在使用RGBD传感器时可以从单个视图进行计算。我们提出了一种新的场景流方法，该方法利用了真实世界场景的局部和分段刚性。通过将运动建模为扭曲场，我们的方法鼓励对刚体运动进行分段平滑求解。我们通过结合使用强度和深度数据，给出了解决局部和整体刚性运动的一般公式。为了有效处理运动的相机，我们将运动建模为刚性分量加上非刚性残差，并提出了交替求解器。评估表明，该方法在最常用的场景流基准测试中获得了最佳结果。通过其他实验，我们表明了该方法在各种不同情况下的普遍适用性。 |
| 课程简介: | Scene flow is defined as the motion field in 3D space, and can be computed from a single view when using an RGBD sensor. We propose a new scene flow approach that exploits the local and piecewise rigidity of real world scenes. By modeling the motion as a field of twists, our method encourages piecewise smooth solutions of rigid body motions. We give a general formulation to solve for local and global rigid motions by jointly using intensity and depth data. In order to deal efficiently with a moving camera, we model the motion as a rigid component plus a non-rigid residual and propose an alternating solver. The evaluation demonstrates that the proposed method achieves the best results in the most commonly used scene flow benchmark. Through additional experiments we indicate the general applicability of our approach in a variety of different scenarios. |
| 关 键 词: | 3D空间; 场景图像 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-30：zyk |
| 最后编审: | 2020-12-30：zyk |
| 阅读次数: | 75 |