图 书 馆
用于光流评估的自然开源电影A Naturalistic Open Source Movie for Optical Flow Evaluation |
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| 课程网址: | http://videolectures.net/eccv2012_butler_optical/ |
| 主讲教师: | Daniel J. Butler |
| 开课单位: | 华盛顿大学 |
| 开课时间: | 2012-11-12 |
| 课程语种: | 英语 |
| 中文简介: |  在真实场景中以自然运动很难测量地面真光流。结果，光流数据集在大小，复杂性和多样性方面受到限制，使得光流算法难以在实际数据上进行训练和测试。我们引入了一个新的光流数据集，该数据集来自开源3D动画短片Sintel。该数据集具有在流行的Middlebury流评估中不具备的重要特征：长序列，大运动，镜面反射，运动模糊，散焦模糊和大气影响。由于生成电影的图形数据是开源的，因此我们能够在复杂度不同的情况下渲染场景，以评估现有流算法失败的地方。我们评估了几种最新的光流算法，发现当前在Middlebury评估中排名靠前的方法难以处理这个更为复杂的数据集，这表明需要对光流估计进行进一步的研究。为了验证合成数据的使用，我们将Sintel的图像和流量统计数据与真实电影和视频的图像和流量统计数据进行比较，并证明它们是相似的。数据集，指标和评估网站是公开可用的。 |
| 课程简介: | Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the image- and flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available. |
| 关 键 词: | 场景渲染; 数据集 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-03-25：zyk |
| 最后编审: | 2021-03-25：zyk |
| 阅读次数: | 62 |