Robust and efficient techniques for texture-less object recognition

This paper presents some of the most widely accredited techniques used in the texture-less object recognition field. We analyze these works to study their methodologies to solving the texture-less problem, while introducing our own state-of-the-art solutions in this genre.

[1]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

[2]  Federico Tombari,et al.  BOLD Features to Detect Texture-less Objects , 2013, 2013 IEEE International Conference on Computer Vision.

[3]  Zhe Wang,et al.  Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search , 2007, VLDB.

[4]  Rafael Grompone von Gioi,et al.  LSD: a Line Segment Detector , 2012, Image Process. Line.

[5]  Kemao Qian,et al.  BORDER: An Oriented Rectangles Approach to Texture-Less Object Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Qian Kemao,et al.  BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[8]  Dima Damen,et al.  Real-time Learning and Detection of 3D Texture-less Objects: A Scalable Approach , 2012, BMVC 2012.

[9]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[10]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.