• Dhanya S Pankaj Department of Earth and Space Sciences Indian Institute of Space Science and Technology Department of Space, Government of India Valiyamala, Thiruvananthapuram - 695 547 Kerala, INDIA
  • Rama Rao Nidamanuri Department of Earth and Space Sciences Indian Institute of Space Science and Technology Department of Space, Government of India Valiyamala, Thiruvananthapuram - 695 547 Kerala, INDIA




3D registration, robust estimation, RANSAC


The 3D modeling pipeline involves registration of partially overlapping 3D scans of an object. The automatic pairwise coarse alignment of partially overlapping 3D images is generally performed using 3D feature matching. The transformation estimation from matched features generally requires robust estimation due to the presence of outliers. RANSAC is a method of choice in problems where model estimation is to be done from data samples containing outliers. The number of RANSAC iterations depends on the number of data points and inliers to the model. Convergence of RANSAC can be very slow in the case of large number of outliers. This paper presents a novel algorithm for the 3D registration task which provides more accurate results in lesser computational time compared to RANSAC. The proposed algorithm is also compared against the existing modifications of RANSAC for 3D pairwise registration. The results indicate that the proposed algorithm tends to obtain the best 3D transformation matrix in lesser time compared to the other algorithms.


Arun K S, Huang T S, Blostein S D (1987). Least-squares fitting of two 3-d point sets.IEEE T Pattern Anal Mach Intell 5:698–700.

Besl P J, McKay ND (1992). Method for registration of 3-d shapes.In Robotics-DL tentative, International Society for Optics and Photonics, 586–606.

Campbell R J, Flynn P J (2001). A survey of freeform object representation and recognition techniques. Computer Vision and Image Understanding 2:166–210.

Chen H, Bhanu B (2007).3d free-form object recognition in range images using local surface patches.Pattern Recognition Letters 28(10):1252–1262.

Chen Y, Medioni G (1992). Object modelling by registration of multiple range images.Image and vision computing 10(3):145–155.

Choi S, Kim T, Yu W (1997). Performance evaluation of ransac family. Journal of Computer Vision 24(3):271–300.

Chua C S, Jarvis R (1997). Point signatures: A new representation for 3d object recognition. International Journal of Computer Vision 25(1):63–85.

Chum O, Matas J (2005). Matching with prosac-progressive sample consensus. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 1:220–226.

Chum O, Matas J, Kittler J (2003). Locally optimized ransac. In: Pattern Recognition,Springer, 236–243.

Dorai C, Wang G, Jain A K, Mercer C (1996). From images to models: Automatic 3d object model construction from multiple views. In: Proceedings of the 13th International Conference on Pattern Recognition, IEEE, 1: 770–774.

Filipe S, Alexandre L A (2013). A comparative evaluation of 3d keypoint detectors.In: 9th Conference on Telecommunications, Conftele, 145–148.

Fischler M A, Bolles R C (1981). Random sample consensus: a paradigm for model fitting with

applications to image analysis and automated cartography. In : Communications of the ACM


Friedman, J H, Bentley J L, Finkel, R A (1977). An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software (TOMS) 3(3):209–226.

Frome A, Huber D, Kolluri R, B¨ulow T, Malik J (2004). Recognizing objects in range data using regional point descriptors. In: Computer Vision-ECCV 2004 Springer, 224–237.

Gomes L, Regina Pereira Bellon O, Silva L (2014). 3d reconstruction methods for digital preservation of cultural heritage: A survey. Pattern Recognition Letters 50:3-14.

Hough P V (1962). Method and means for recognizing complex patterns. US Patent 3,069,654.

Johnson A E, Hebert M (1999). Using spin images for efficient object recognition in cluttered 3d scenes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 21(5):433–449.

Klasing K, Althoff D, Wollherr D, Buss M (2009). Comparison of surface normal estimation methods for range sensing applications. In: Proceedings of IEEE International Conference on Robotics and Automation, 3206–3211.

Knopp J, Prasad M, Willems G, Timofte R, Van Gool L (2010). Hough transform and 3d surf for robust three dimensional classification. In: Computer Vision–ECCV 2010 Springer, 589–602.

Koenderink J J, van Doorn A J (1992). Surface shape and curvature scales. Image and vision computing 10(8):557–564.

Mian A, Bennamoun M, Owens R (2010). On the repeatability and quality of keypoints for local featurebased 3d object retrieval from cluttered scenes. International Journal of Computer Vision 89(2-3):348–361.

Mian A, Bennamoun M, Owens R (2006). Three dimensional model-based object recognition and segmentation in cluttered scenes.Pattern Analysis and Machine Intelligence, IEEE Transactions on 28(10):1584–1601.

Muja M (2009). FLANN-fast library for approximate nearest neighbors. User Manual.

Nasuto D, Craddock J B R(2002). Napsac: High noise, high dimensional robust estimation-its in the bag. In: BMVC, 2:458–467.

Pulli K (1999). Multiview registration for large data sets. In: IEEE Proceedings of Second International Conference on 3-D Digital Imaging and Modeling, 160–168.

Rousseeuw P J (1984). Least median of squares regression. Journal of the American statistical association 79(388):871–880.

Rousseeuw P J, Leroy A M (2005). Robust regression and outlier detection, vol. 589. John Wiley & Sons.

Rusinkiewicz S, Levoy M (2001). Efficient variants of the icp algorithm. In: IEEE Proceedings of Third International Conference on 3-D Digital Imaging and Modeling, 145–152.

Rusu R B, Blodow N, Beetz M (2009). Fast point feature histograms (fpfh) for 3d registration. In: Proceesings of IEEE International Conference on Robotics and Automation, 3212–3217.

Rusu R B, Blodow N, Marton Z C, Beetz M(2008). Aligning point cloud views using persistent feature histograms. In: IROS 2008. IEEE/RSJ International Conference onIntelligent Robots and Systems, 3384–3391.

Rusu R B, Cousins S (2011). 3d is here: Point cloud library (pcl). IEEE International Conference on Robotics and Automation (ICRA), 1–4.

Salti S, Tombari F, Di Stefano L (2011). A performance evaluation of 3d keypoint detectors. In: IEEE International Conference on3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 236–243.

Stein F, Medioni G (1992). Structural indexing: Efficient 3-d object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2):125–145.

Tam G K, Cheng Z Q, Lai Y K, Langbein F C, Liu Y, Marshall D, Martin R R, Sun X F, Rosin P L (2013). Registration of 3d point clouds and meshes: A survey from rigid to nonrigid. Visualization and Computer Graphics, IEEE Transactions on 19(7): 1199–1217.

Tombari F, Salti S, Di Stefano L (2010). Unique signatures of histograms for local surface description. In: Computer Vision–ECCV Springer, 356–369.

Torr P H, Zisserman A (2000). Mlesac: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding 78(1):138–156.

Turk G, Levoy M (2005). The stanford 3d scanning repository. Umeyama S (1991). Least-squares estimation of transformation parameters between two point patterns.IEEE Transactions on pattern analysis and machine intelligence 4:376–380.

Wang H (2004). Robust statistics for computer vision: model fitting, image segmentation and visual motion analysis. Monash University.

Zhong Y (2009). Intrinsic shape signatures: A shape descriptor for 3d object recognition.In: Computer VisionWorkshops (ICCVWorkshops), 2009 IEEE 12th International Conference on, 689–696.




How to Cite

Pankaj, D. S., & Nidamanuri, R. R. (2016). A ROBUST ESTIMATION TECHNIQUE FOR 3D POINT CLOUD REGISTRATION. Image Analysis & Stereology, 35(1), 15–28. https://doi.org/10.5566/ias.1378



Original Research Paper