Super Generalized 4PCS for 3D Registration

被引:59
作者
Mohamad, Mustafa [1 ]
Ahmed, Mirza Tahir [2 ]
Rappaport, David [1 ]
Greenspan, Michael [1 ,2 ]
机构
[1] Queens Univ, Sch Comp, Kingston, ON K7L 3N6, Canada
[2] Queens Univ, Dept Elect & Comp Engn, Kingston, ON K7L 3N6, Canada
来源
2015 INTERNATIONAL CONFERENCE ON 3D VISION | 2015年
关键词
OBJECT RECOGNITION; POSE ESTIMATION;
D O I
10.1109/3DV.2015.74
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The 4-Points Congruent Sets ( 4PCS) Algorithm is an established approach to registering two overlapping 3D point sets with partial overlap and arbitrary initial poses. 4PCS performs the registration efficiently using a special set of 4 points, also known as a base, formed by two co-planar pairs of points within a RANSAC framework. The SUPER 4PCS algorithm uses intelligent indexing to reduce the complexity of the original 4PCS algorithm. Although SUPER 4PCS is efficient, we show in this work that one can gain significant practical improvements in runtime by reducing the number of congruent 4-point bases across the two 3D point sets. We accomplish this by using a generalized 4-point base which considers non-coplanar 4-point bases as well as planar ones. We show through experimentation that the number of 4-point bases decreases, sometimes exponentially, with a non-coplanar base. Using this property, we propose the Super Generalized 4PCS algorithm which can exhibit a significant speed-up of up to 6.5x over the Super 4PCS algorithm as demonstrated experimentally.
引用
收藏
页码:598 / 606
页数:9
相关论文
共 26 条
[1]   4-points congruent sets for robust pairwise surface registration [J].
Aiger, Dror ;
Mitra, Niloy J. ;
Cohen-Or, Daniel .
ACM TRANSACTIONS ON GRAPHICS, 2008, 27 (03)
[2]  
Amberg B, 2007, IEEE I CONF COMP VIS, P1326
[3]  
[Anonymous], CORR
[4]   A METHOD FOR REGISTRATION OF 3-D SHAPES [J].
BESL, PJ ;
MCKAY, ND .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1992, 14 (02) :239-256
[5]   Sparse Iterative Closest Point [J].
Bouaziz, Sofien ;
Tagliasacchi, Andrea ;
Pauly, Mark .
COMPUTER GRAPHICS FORUM, 2013, 32 (05) :113-123
[6]  
Choi C, 2012, IEEE INT C INT ROBOT, P3342, DOI 10.1109/IROS.2012.6386067
[7]  
Choi C, 2012, IEEE INT CONF ROBOT, P1724, DOI 10.1109/ICRA.2012.6225371
[8]   Optimal Randomized RANSAC [J].
Chum, Ondrej ;
Matas, Jiri .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2008, 30 (08) :1472-1482
[9]   Model Globally, Match Locally: Efficient and Robust 3D Object Recognition [J].
Drost, Bertram ;
Ulrich, Markus ;
Navab, Nassir ;
Ilic, Slobodan .
2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2010, :998-1005
[10]   RANDOM SAMPLE CONSENSUS - A PARADIGM FOR MODEL-FITTING WITH APPLICATIONS TO IMAGE-ANALYSIS AND AUTOMATED CARTOGRAPHY [J].
FISCHLER, MA ;
BOLLES, RC .
COMMUNICATIONS OF THE ACM, 1981, 24 (06) :381-395