WSDF: Weighting of Signed Distance Function for Camera Motion Estimation in RGB-D Data

With the recent advent of the cost-effective Kinect, which can capture real-time high-resolution RGB and visual depth information, has opened an opportunity to significantly increase the capabilities of many automated vision based recognition including object/action classification, 3D reconstruction, etc… In this work, we address the camera motion estimation which is an important phase in 3D object reconstruction system based on RGB-D data. We segment objects by thresholding algorithm based on depth data and propose the weighting function for SDF that is called WSDF. The problem of minimizing of this function is solved by Gauss-Newton methods. We systematically evaluate our method on TUM dataset. The experimental results are measured by ATE and RPE that evaluate both global and local consistency of camera motion estimation algorithm. We demonstrate large improvements over the state-of-the-art methods on both plant and teddy3 objects and achieve the best ATE as 0.00564 and 0.0182 and the best RPE as 0.00719 and 0.00104, respectively. These experiments show that the proposed method significantly outperforms state-of-the-art techniques.