Dense 3D point-cloud model using optical flow for a monocular reconstruction system

Abstract

In this paper, we present an enhanced 3D reconstruction algorithm designed to support an autonomously navigated unmanned ground vehicle. An unmanned system can use the technique to construct a point cloud model of its unknown surroundings. The algorithm presented focuses on the 3D reconstruction of a scene using image sequences captured by only a single moving camera. The original reconstruction process, resulting with a point cloud, was computed utilizing extracted and matched Speeded Up Robust Feature (SURF) points from subsequent video frames. Using depth triangulation analysis, we were able to compute the depth of each feature point within the scene. We concluded that although SURF points are accurate and extremely distinctive, the number of points extracted and matched was not sufficient for our applications. A sparse point cloud model hinders the ability to do further processing for the autonomous system such as object recognition or self-positioning. We present an enhanced version of the algorithm which increases the number of points within the model while maintaining the near real-time computational speeds and accuracy of the original sparse reconstruction. We do so by generating points using both global image characteristics and local SURF feature neighborhood information. Specifically, we generate optical flow disparities using the Horn-Schunck optical flow estimation technique and evaluate the quality of these features for disparity calculations using the SURF keypoint detection method. Areas of the image that locate within SURF feature neighborhoods are tracked using optical flow and used to compute an extremely dense model. The enhanced model contains the high frequency details of the scene that allow for 3D object recognition. The main contribution of the newly added preprocessing steps is measured by evaluating the density and accuracy of the reconstructed point cloud model in relation to real-world measurements.