Fast adaptive frame preprocessing for 3D reconstruction

This paper presents a new online preprocessing strategy to detect and discard ongoing bad frames in video sequences. These include frames where an accurate localization between corresponding points is difficult, such as for blurred frames, or which do not provide relevant information with respect to the previous frames in terms of texture, image contrast and non-flat areas. Unlike keyframe selectors and deblurring methods, the proposed approach is a fast preprocessing working on a simple gradient statistic, that does not require to compute complex time-consuming image processing, such as the computation of image feature keypoints, previous poses and 3D structure, or to know a priori the input sequence. The presented method provides a fast and useful frame pre-analysis which can be used to improve further image analysis tasks, including also the keyframe selection or the blur detection, or to directly filter the video sequence as shown in the paper, improving the final 3D reconstruction by discarding noisy frames and decreasing the final computation time by removing some redundant frames. This scheme is adaptive, fast and works at runtime by exploiting the image gradient statistic of the last few frames of the video sequence. Experimental results show that the proposed frame selection strategy is robust and improves the final 3D reconstruction both in terms of number of obtained 3D points and reprojection error, also reducing the computational time.