Depth-assisted rectification for real-time object detection and pose estimation
Depth-assisted rectification for real-time object detection and pose estimation
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DOI：
10.1007/s00138-015-0740-8
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发表时间：
2015-12
影响因子：
3.3
通讯作者：
J. P. Lima;Francisco Simões;Hideaki Uchiyama;V. Teichrieb;É. Marchand
中科院分区：
文献类型：
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作者：
J. P. Lima;Francisco Simões;Hideaki Uchiyama;V. Teichrieb;É. Marchand
RGB-D传感器近年来已成为普通用户易于使用的产品。它们提供场景的彩色图像和深度图像，并且除了用于对象建模之外，它们还可以为对象检测和真实的跟踪提供重要线索。在这种情况下，本文提出的工作研究了使用消费者RGB-D传感器进行物体检测和自然特征的姿态估计。提出了两种基于深度辅助校正的方法，该方法利用深度数据将从彩色图像中提取的特征变换为规范视图，以获得对旋转、缩放和透视失真不变性的表示。虽然一种方法适用于平面或非平面的纹理对象，但另一种方法侧重于无纹理的平面对象。所提出的方法进行了定性和定量的评价，表明他们可以获得更好的结果比一些现有的方法进行目标检测和姿态估计，特别是当处理倾斜的姿态。
RGB-D sensors have become in recent years a product of easy access to general users. They provide both a color image and a depth image of the scene and, besides being used for object modeling, they can also offer important cues for object detection and tracking in real time. In this context, the work presented in this paper investigates the use of consumer RGB-D sensors for object detection and pose estimation from natural features. Two methods based on depth-assisted rectification are proposed, which transform features extracted from the color image to a canonical view using depth data in order to obtain a representation invariant to rotation, scale and perspective distortions. While one method is suitable for textured objects, either planar or non-planar, the other method focuses on texture-less planar objects. Qualitative and quantitative evaluations of the proposed methods are performed, showing that they can obtain better results than some existing methods for object detection and pose estimation, especially when dealing with oblique poses.