Research on 3D Reverse Technology Based on Monocular Image Sequence

A solution to improve the FAST feature point detection algorithm susceptible to image noise interference is proposed. Eight directions are derived from the selected point to solve the gray level mutations in these eight directions, and based on this, it is judged whether the point is a feature point. And take the maximum value suppression for the dense area of feature points, and solve the maximum value of the sum of gray changes in all directions as the judgment condition to screen the feature points. A new feature point matching scheme is proposed, where the Hamming distance between feature points within the target image is derived by the BruteForce algorithm. If the Hamming distance between the first few target points and the original feature point is less than a certain threshold, it will be substituted into the original image to solve the problem. The Hamming distance between the feature points of the original image is selected as the correct matching point. Finally, the principle of region growth is introduced to the MC algorithm, which saves the amount of calculation and improves the generation accuracy.

[1]  R. Sukthankar,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[2]  Li Chao,et al.  An improved method for eliminating false matches , 2017, 2017 2nd International Conference on Image, Vision and Computing (ICIVC).

[3]  Juan D. Tardós,et al.  ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras , 2016, IEEE Transactions on Robotics.

[4]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Richard I. Hartley,et al.  Optimised KD-trees for fast image descriptor matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Andrew W. Fitzgibbon,et al.  KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[7]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Derek D. Lichti,et al.  An on-site approach for the self-calibration of terrestrial laser scanner , 2014 .

[9]  Lu Lu,et al.  High precision camera calibration in vision measurement , 2007 .

[10]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.

[11]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[12]  J. M. M. Montiel,et al.  ORB-SLAM: A Versatile and Accurate Monocular SLAM System , 2015, IEEE Transactions on Robotics.

[13]  Riyanto Sigit,et al.  3D heart image reconstruction and visualization with marching cubes algorithm , 2016, 2016 International Conference on Knowledge Creation and Intelligent Computing (KCIC).

[14]  Pascal Fua,et al.  On benchmarking camera calibration and multi-view stereo for high resolution imagery , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Changchang Wu,et al.  Towards Linear-Time Incremental Structure from Motion , 2013, 2013 International Conference on 3D Vision.

[16]  Jitendra Malik,et al.  Modeling and Rendering Architecture from Photographs: A hybrid geometry- and image-based approach , 1996, SIGGRAPH.

[17]  Dieter Fox,et al.  DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[19]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Hanqing Lu,et al.  Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Hujun Bao,et al.  Robust Keyframe-Based Monocular SLAM for Augmented Reality , 2016, 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct).

[22]  Zheng Hong,et al.  SIFT matching method based on K nearest neighbor support feature points , 2016, 2016 IEEE International Conference on Signal and Image Processing (ICSIP).

[23]  Yasuyuki Matsushita,et al.  GMS: Grid-Based Motion Statistics for Fast, Ultra-robust Feature Correspondence , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  John J. Leonard,et al.  Real-time large-scale dense RGB-D SLAM with volumetric fusion , 2014, Int. J. Robotics Res..

[25]  Zhanyi Hu,et al.  An improved PMVS through scene geometric information , 2011 .