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分析杂乱图像中的三维对象

Analyzing 3D Objects in Cluttered Images
课程网址: http://videolectures.net/machine_hejrati_analyzing_objects/  
主讲教师: Mohsen Hejrati
开课单位: 加州大学欧文分校
开课时间: 2013-06-14
课程语种: 英语
中文简介:
我们提出了一种检测和分析具有重度遮挡和杂波的真实世界图像中的对象的3D配置的方法。我们专注于寻找和分析汽车的应用。我们用两阶段模型这样做;关于2D形状和外观变化的第一阶段原因是由于车内变化(车辆车厢看起来不同于轿车)和视点的变化。我们不是使用基于视图的模型,而是使用少量基于本地视图的模板来描述使用少量基于本地视图的模板来模拟大量有效视图和形状的组合表示。我们使用该模型来提出候选检测和2D形状估计。然后使用显式的形状和视点三维模型,在第二阶段对这些估计进行细化。我们使用可变形模型在类变体中捕获3D,并使用弱透视相机模型来捕获视点。我们从2D注释中学习所有模型参数。我们展示了PASCAL VOC 2011数据集中具有挑战性的图像的检测,视点估计和3D形状重建的最新精度。
课程简介: We present an approach to detecting and analyzing the 3D configuration of objects in real-world images with heavy occlusion and clutter. We focus on the application of finding and analyzing cars. We do so with a two-stage model; the first stage reasons about 2D shape and appearance variation due to within-class variation(station wagons look different than sedans) and changes in viewpoint. Rather than using a view-based model, we describe a compositional representation that models a large number of effective views and shapes using a small number of local view-based templates. We use this model to propose candidate detections and 2D estimates of shape. These estimates are then refined by our second stage, using an explicit 3D model of shape and viewpoint. We use a morphable model to capture 3D within-class variation, and use a weak-perspective camera model to capture viewpoint. We learn all model parameters from 2D annotations. We demonstrate state-of-the-art accuracy for detection, viewpoint estimation, and 3D shape reconstruction on challenging images from the PASCAL VOC 2011 dataset.
关 键 词: 重度遮挡; 真实世界; 本地视图
课程来源: 视频讲座网
最后编审: 2019-05-15:cwx
阅读次数: 69