Dense 3D point cloud representation of a scene using uncalibrated monocular vision


Yakov Diskin

Date of Award


Degree Name

M.S. in Electrical Engineering


Department of Electrical and Computer Engineering


Advisor: Vijayan K. Asari


We present a 3D reconstruction algorithm designed to support various automation and navigation applications. The algorithm presented focuses on the 3D reconstruction of a scene using only a single moving camera. Utilizing video frames captured at different points in time allows us to determine the depths of a scene. In this way, the system can be used to construct a point cloud model of its unknown surroundings. In this thesis, we present the step by step methodology of the development of a reconstruction technique. The original reconstruction process, resulting with a point cloud was computed based on feature matching and depth triangulation analysis. In an improved version of the algorithm, we utilized optical flow features to create an extremely dense representation model. Although dense, this model is hindered due to its low disparity resolution. As feature points were matched from frame to frame, the resolution of the input images and the discrete nature of disparities limited the depth computations within a scene. With the third algorithmic modification, we introduce the addition of the preprocessing step of nonlinear super resolution. With this addition, the accuracy of the point cloud which relies on precise disparity measurement has significantly increased. Using a pixel by pixel approach, the super resolution technique computes the phase congruency of each pixel's neighborhood and produces nonlinearly interpolated high resolution input frames. Thus, a feature point travels a more precise discrete disparity. Also, the quantity of points within the 3D point cloud model is significantly increased since the number of features is directly proportional to the resolution and high frequencies of the input image. Our final contribution of additional preprocessing steps is designed to filter noise points and mismatched features, giving birth to the complete Dense Point-cloud Representation (DPR) technique. We measure the success of DPR by evaluating the visual appeal, density, accuracy and computational expense of the reconstruction technique and compare with two state-of-the-arts techniques. After the presentation of rigorous analysis and comparison, we conclude by presenting the future direction of development and its plans for deployment in real-world applications.


Three-dimensional imaging Mathematical models, Image reconstruction Mathematical models, Computer vision Mathematical models, Electrical engineering; engineering; monocular vision; 3D scene reconstruction; dense point-cloud representation; point cloud model; DPR; super resolution; vision lab; computer vision; vision navigation; UAV; UAS; UGV; RAIDER; depth resolution enhancement

Rights Statement

Copyright 2013, author