Depth map recovery based on optimal scheduling of invalid point filling
Xinli Xiong, Dongyue Chen, Wuwei Zhang, Tong Jia · 2018
This paper proposes a recovery method which is commendably able to fill the invalid points in the depth image obtained with Kinectv2. Two primary tasks of the invalid points filling process are filling priority evaluation and optimal filling value prediction. At first, we put forward an entropy-based measurement for the filling priority. Then a metric of entropy that combines Valid Point Number and Neighboring Standard Deviation is proposed to sort all the invalid points. Secondly, the optimal prediction of the filling value is calculated by finding the valid neighbor that is most similar to the average of its neighborhood. Finally, the proposed method is applied to each invalid point to complete the recovery of the depth image. Experimental results show that our method can effectively fill the invalid points in the depth image and performs better than some competitive algorithms in accuracy and robustness.