Representative feature descriptor sets for robust handheld camera localization
Representative feature descriptor sets for robust handheld camera localization
复制标题
用于稳健手持相机定位的代表性特征描述符集
DOI：
10.1109/ismar.2012.6402540
复制
发表时间：
2012
期刊：
影响因子：
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通讯作者：
Selim Benhimane
中科院分区：
文献类型：
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作者：
Daniel Kurz;Thomas Olszamowski;Selim Benhimane
我们提出了一种方法来自动确定一组特征描述符，描述了一个对象，使它可以被本地化的各种观点。基于一组合成生成的视图，检测局部图像特征，并在数据库中进行描述和聚合。我们提出的方法评估这些数据库特征之间的匹配，最终从数据库中找到一组最具代表性的描述符。使用这种可扩展的离线过程，本地化的成功率显着增加，而不会增加计算负载的运行时方法。此外，如果相机定位相对于在一个已知的重力方向的对象进行，我们建议创建多个参考描述符集相机的主轴和重力矢量之间的不同角度。这种方法特别适合于具有内置惯性传感器的手持设备，并且能够与仅包含与所测量的重力一致的相机姿态相关的信息的参考数据集进行匹配。使用大量的真实的相机图像，各种对象，不同的相机和不同种类的特征描述符的综合评价所提出的方法证实，我们的方法优于标准的特征描述符为基础的方法。
We present a method to automatically determine a set of feature descriptors that describes an object such that it can be localized under a variety of viewpoints. Based on a set of synthetically generated views, local image features are detected, described and aggregated in a database. Our proposed method evaluates matches between these database features to eventually find a set of the most representative descriptors from the database. Using this scalable offline process, the localization success rate is significantly increased without adding computational load to the runtime method. Moreover, if camera localization is performed with respect to objects at a known gravity orientation, we propose to create multiple reference descriptor sets for different angles between the camera's principal axis and the gravity vector. This approach is particularly suited for handheld devices with built-in inertial sensors and enables matching against a reference dataset only containing the information relevant for camera poses that are consistent with the measured gravity. Comprehensive evaluations of the proposed methods using a large quantity of real camera images, a variety of objects, different cameras and different kinds of feature descriptors confirm that our approaches outperform standard feature descriptor-based methods.