Year: 2013
Type: article
Abstract: 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 v 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.vi This thesis is dedicated to: My parents, Boris and Olga Diskin, who have supported me through all circumstances and have given me everything they could to ensure my success in life.My family, who have taught me determination, focus and will power throughout life and have challenged my arguments, thoughts and patience.My dearest friends, on whom I frequently rely on to clear my head, relieve stress or simply to find a reason to procrastinate, and who bring constant joy and never ending memories.Dr. Vijayan Asari and the Vision Lab crew, by guiding me as a beacon into the professional world, who have advised me and surrounded me with greatest intellectuals in our research field."If I have seen further than others, it is by standing upon the shoulders of giants."-Sir Isaac Newton "The true sign of intelligence is not knowledge but imagination.""If you can't explain it simply, you don't understand it well enough."
Author: Yakov Diskin
Institution: University of Dayton
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2613742383