Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization
利用回归森林中的点和线进行RGB-D相机重新定位
张宁
https://pan.baidu.com/s/17RfrQqp0LaCELabma4jVDQ
Lili Meng , Frederick Tung , James J. Little , Julien Valentin , Clarence W. de Silva
Abstract— Camera relocalization plays a vital role in many robotics and computer vision applications, such as self-driving cars and virtual reality. Recent random forests based methods exploit randomly sampled pixel comparison features to predict 3D world locations for 2D image locations to guide the camera pose optimization. However, these point features are only sampled randomly in images, without considering geometric information such as lines, leading to large errors with the existence of poorly textured areas or in motion blur. Line segments are more robust in these environments. In this work, we propose to jointly exploit points and lines within the framework of uncertainty driven regression forests. The proposed approach is thoroughly evaluated on three publicly available datasets against several strong state-of-the-art baselines in terms of several different error metrics. Experimental results prove the efficacy of our method, showing superior or on-par state-of-the-art performance.
相机重定位在许多机器人和计算机视觉应用中起着至关重要的作用，例如自动驾驶汽车和虚拟现实。最近基于随机森林的方法利用随机采样的像素比较特征来预测2D图像位置的3D世界位置以指导相机姿势优化。然而，这些点特征仅在图像中随机采样，而不考虑诸如线的几何信息，导致存在纹理不良区域或运动模糊的大误差。线段在这些环境中更加强大。 在这项工作中，我们建议在不确定性驱动的回归森林框架内共同利用点和线。根据几个不同的错误指标，针对几个强大的最新基准，对三个公开可用的数据集进行了全面评估。 实验结果证明了我们的方法的功效，显示出优越或相近的最先进性能。
在这项工作中，我们建议在不确定性驱动的回归森林框架内利用线性和点特征。我们同时在统一的相机姿态优化框架中考虑点和线预测。我们在具有不同空间尺度和动态的三个数据集中广泛评估所提出的方法。实验结果证明了所开发方法的功效，显示出优越的现有技术或同等性能。 此外，彻底展示了不同的故障情况，为未来可能的工作提供了一些启示。