图 书 馆
三维的目标检测与可变形的三维长方体模型的观点估计3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model |
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| 课程网址: | http://videolectures.net/machine_fidler_object_detection/ |
| 主讲教师: | Sanja Fidler |
| 开课单位: | 多伦多大学 |
| 开课时间: | 2013-01-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This paper addresses the problem of category-level 3D object detection. Given a monocular image, our aim is to localize the objects in 3D by enclosing them with tight oriented 3D bounding boxes. We propose a novel approach that extends the well-acclaimed deformable part-based model[Felz.] to reason in 3D. Our model represents an object class as a deformable 3D cuboid composed of faces and parts, which are both allowed to deform with respect to their anchors on the 3D box. We model the appearance of each face in fronto-parallel coordinates, thus effectively factoring out the appearance variation induced by viewpoint. Our model reasons about face visibility patters called aspects. We train the cuboid model jointly and discriminatively and share weights across all aspects to attain efficiency. Inference then entails sliding and rotating the box in 3D and scoring object hypotheses. While for inference we discretize the search space, the variables are continuous in our model. We demonstrate the effectiveness of our approach in indoor and outdoor scenarios, and show that our approach outperforms the state-of-the-art in both 2D[Felz09] and 3D object detection[Hedau12]. |
| 关 键 词: | 三维目标检测; 可变形部件模型; 锚变形 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-27：lxf |
| 阅读次数: | 96 |