3D量測技術在近幾年來快速的發展，現今最流行的系統大多基於雷射感應器，主要是因為雷射能提供即時且精準的資訊；但相較於傳統的攝影機，這些3D量測儀器價格通常較高、也較難以取得，而且雷射測距儀器非常容易被相同波長的雷射所干擾。另一方面，基於影像處理的3D量測方法利用攝影機的位移所產生的視差進行配對，然後藉由估計出攝影機的位移來進一步的推算3D點的座標位置，免去了加裝任何實際3D偵測裝置的要求，但也因為是使用數學方法計算出3D座標，此類系統要達到即時運算的能力通常得倚靠高度的平行化運算，因此這類系統往往較難以應用到攜帶、穿戴性裝置上。
在本論文中，受Structure from Motion系統啟發，為了進一步提升運算效率，我們提出一個系統使用單眼視覺(Mono Vision)的連續影像來進行運算，重建稀疏特徵點成為3D點雲圖。此系統會追蹤目前畫面中每個特徵點的位移量以及數量變化，再根據這些位移量以及數量變化挑出關鍵影格，因為我們只在關鍵影格上作估算攝影機位置的動作，所以能夠大量減少運算所需的時間以及抵抗攝影機雜訊的能力；更進一步的，為了防止系統重複創建出相同的3D點，系統會持續追蹤特徵點的位置，直到特徵點超出攝影機的可視範圍之外才會重建此特徵點。最後，根據實驗結果，我們成功的提出了一個能夠快速運算的系統，並且擁有良好的準確度以及能夠產生更為密集的點雲圖。

Accurate 3D measuring system thrives in the past few years. Most of them are based on laser scanners because these laser scanners are able to acquire 3D information directly and precisely in real time. However, comparing to the conventional cameras, this kinds of equipment are usually expensive and they are not commonly available to customers. Moreover, laser scanners interfere easily with each other sensors of the same type. On the other hand, computer vision based 3D measuring techniques use stereo matching to acquire the cameras’ relative position and then estimate the 3D location of points on the image. Because this kind of systems needs additional estimation of the 3D information, systems with real time capability often relies on heavy parallelism that prevents implementation on mobile devices.
Inspired by the structure from motion systems, we propose a system that reconstructs sparse feature points to a 3D point cloud using a mono video sequence so as to achieve higher computation efficiency. The system keeps tracking all detected feature points and calculates both the amount of these feature points and their moving distances. We only use the key frames to estimate the current position of the camera in order to reduce the computation load and the noise interference on the system. Furthermore, for the sake of avoiding duplicate 3D points, the system reconstructs the 2D point only when the point shifts out of the boundary of a camera. In our experiments, we show that our system is able to achieve state-of-the-art accuracy and a denser point cloud with a high speed.

[1] A. Fenster and D. B. Downey, "Fast Parametric Elastic Image Registration," IEEE Engineering in Medicine and Biology Magazine, vol. 15, no. 6, pp. 41-51, 2002.
[2] J. M. Lopez-Sanchez and J. Fortuny-Guasch, "3-D radar Imaging Using Range Migration Techniques," IEEE Transactions on Antennas and Propagation, vol. 48, no. 5, pp. 728-737, 2002.
[3] B. Douillard, J. Underwood, N. Kuntz, V. Vlaskine, A. Quadros, P. Morton and A. Frenkel, "On the segmentation of 3D LIDAR point clouds," in IEEE International Conference on Robotics and Automation, Shanghai, 2011.
[4] F. Endres, J. Hess, N. Engelhard and J. Sturm, "An Evaluation of the RGB-D SLAM System," in IEEE International Conference on Robotics and Automation (ICRA), Saint Paul, MN, 2012.
[5] J. Chen, D. Bautembach and S. Izadi, "Scalable Real-time Volumetric Surface Reconstruction," ACM Transactions on Graphics - SIGGRAPH 2013 Conference Proceedings, vol. 32, no. 4, 2013.
[6] Q.-Y. Zhou and V. Koltun, "Dense Scene Reconstruction with Points of Interest," ACM Transactions on Graphics - SIGGRAPH 2013 Conference Proceedings, vol. 32, no. 4, 2013.
[7] M. Nießner, M. Zollhöfer, S. Izadi and M. Stamminger, "Real-time 3D Reconstruction at Scale Using Voxel Hashing," ACM Transactions on Graphics - Proceedings of ACM SIGGRAPH Asia, vol. 32, no. 6, 2013.
[8] S. M. Seitz, B. Curless, J. Diebel and D. Scharstein, "A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006.
[9] B. Micusik and J. Kosecka, "Piecewise planar city 3D modeling from street view panoramic sequences," in IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, 2009.
[10] A. Geiger, J. Ziegler and C. Stiller, "StereoScan: Dense 3d Reconstruction in Real-time," in IEEE Intelligent Vehicles Symposium, Baden-Baden, 2011.
[11] G. Klein and D. Murray, "Parallel Tracking and Mapping for Small AR Workspaces," in ISMAR '07 Proceedings of the 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, 2007.
[12] N. Snavely, S. M. Seitz and R. Szeliski, "Modeling the World from Internet Photo Collections," International Journal of Computer Vision, vol. 80, no. 2, pp. 189-210, 2008.
[13] S. Agarwal, Y. Furukawa, N. Snavely, I. Simon, B. Curless, S. M. Seitz and R. Szeliski, "Building Rome in a Day," Communications of the ACM, vol. 54, no. 10, pp. 105-112, 2011.
[14] C. Wu, "Towards Linear-Time Incremental Structure from Motion," in International Conference on 3D Vision, Seattle, WA, 2013.
[15] P. Moulon, P. Monasse and R. Marlet, "Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion," in IEEE International Conference on Computer Vision, Sydney, NSW, 2013.
[16] A. Saxena, M. Sun and A. Y. Ng, "Learning 3-D Scene Structure from a Single Still Image," in IEEE 11th International Conference on Computer Vision, Rio de Janeiro, 2007.
[17] A. Gupta, A. A. Efros and M. Hebert, "Blocks World Revisited: Image Understanding Using Qualitative Geometry and Mechanics," Computer Vision - ECCV 2010: 11th European Conference on Computer Vision, pp. 482-496, 2010.
[18] A. J. Davison, I. D. Reid, N. D. Molton and O. Stasse, "MonoSLAM: Real-Time Single Camera SLAM," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1052-1067, 2007.
[19] A. Akbarzadeh, J. -M. Frahm, P. Mordohai, B. Clipp, C. Engels, D. Gallup, P. Merrell, M. Phelps, S. Sinha, B. Talton, L. Wang, Q. Yang, H. Stewenius, R. Yang, G. Welch, H. Towles, D. Nister and M. Pollefeys, "Towards Urban 3D Reconstruction from Video," in Third International Symposium on 3D Data Processing, Visualization, and Transmission, Chapel Hill, NC, 2006.
[20] J.-M. Frahm, M. Pollefeys, S. Lazebnik, D. Gallup, B. Clipp, R. Raguram, C. Wu, C. Zach and T. Johnson, "Fast Robust Large-scale Mapping from Video and Internet Photo Collections," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 65, no. 6, pp. 538-549, 2010.
[21] D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, pp. 91-110, 2004.
[22] H. Bay, T. Tuytelaars and L. V. Gool, "Speeded-Up Robust Features (SURF)," Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, June 2008.
[23] E. Rublee, V. Rabaud, K. Konolige and G. Bradski, "ORB: An efficient alternative to SIFT or SURF," in International Conference on Computer Vision, Barcelona, 2011.
[24] P. F. Alcantarilla, J. Nuevo and A. Bartoli, "Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces," in British Machine Vision Conference (BMVC), Bristol, UK, September 2013.
[25] E. Rosten and T. Drummond, "Machine Learning for High-Speed Corner Detection," in Proceedings of the 9th European conference on Computer Vision, 2006.
[26] J.-y. Bouguet, "Pyramidal Implementation of the Lucas Kanade Feature Tracker," Intel Corporation, Microprocessor Research Labs, 2000.
[27] S. M. Smith and J. M. Brady, "SUSAN—A New Approach to Low Level Image Processing," International Journal of Computer Vision, vol. 23, pp. 45-78, May 1997.
[28] C. Harris and M. Stephens, "A Combined Corner and Edge Detector," in 4th Alvey Vision Conference, 1988.
[29] J. R. Quinlan, Induction of Decision Trees, vol. 1, Kluwer Academic Publishers, 1986, pp. 81-106.
[30] B. D. Lucas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision," in Proceedings of the 7th international joint conference on Artificial intelligence, 1981.
[31] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, second ed., Cambridge University Press, 2004.
[32] M. A. Fischler and R. C. Bolles, "Random Sample Consensus: a Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography.," Communications of the ACM, vol. 26, pp. 381-395, June 1981.
[33] J. Nocedal and S. Wright, Numerical Optimization, Springer-Verlag New York, 2006.
[34] C. Zach, "Robust Bundle Adjustment Revisited," Computer Vision, vol. 8693, pp. 772-787, 2014.