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Rotational Projection Statistics for 3D Local Surface Description and Object Recognition

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Abstract

Recognizing 3D objects in the presence of noise, varying mesh resolution, occlusion and clutter is a very challenging task. This paper presents a novel method named Rotational Projection Statistics (RoPS). It has three major modules: local reference frame (LRF) definition, RoPS feature description and 3D object recognition. We propose a novel technique to define the LRF by calculating the scatter matrix of all points lying on the local surface. RoPS feature descriptors are obtained by rotationally projecting the neighboring points of a feature point onto 2D planes and calculating a set of statistics (including low-order central moments and entropy) of the distribution of these projected points. Using the proposed LRF and RoPS descriptor, we present a hierarchical 3D object recognition algorithm. The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets. Our proposed techniques exhibited superior performance compared to existing techniques. We also showed that our method is robust with respect to noise and varying mesh resolution. Our RoPS based algorithm achieved recognition rates of 100, 98.9, 95.4 and 96.0 % respectively when tested on the Bologna, UWA, Queen’s and Ca’ Foscari Venezia Datasets.

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References

  • Atmosukarto, I., & Shapiro, L. (2010). 3D object retrieval using salient views. In Proceedings of the First ACM International Conference on Multimedia, Information Retrieval (pp. 73–82). Vancouver, Canada.

  • Bariya, P., & Nishino, K. (2010). Scale-hierarchical 3D object recognition in cluttered scenes. In IEEE Conference on Computer Vision and, Pattern Recognition (pp. 1657–1664). San Francisco, CA.

  • Bariya, P., Novatnack, J., Schwartz, G., & Nishino, K. (2012). 3D geometric scale variability in range images: Features and descriptors. International Journal of Computer Vision, 99(2), 232–255.

    Article  MathSciNet  Google Scholar 

  • Bayramoglu, N., & Alatan, A. (2010). Shape index SIFT: Range image recognition using local features. In 20th International Conference on, Pattern Recognition (pp. 352–355). Istanbul, Turkey.

  • Belongie, S., Malik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(4), 509–522.

    Article  Google Scholar 

  • Bentley, J. (1975). Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9), 509–517.

    Article  MathSciNet  MATH  Google Scholar 

  • Besl, P., & McKay, N. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239–256.

    Article  Google Scholar 

  • Boyer, E., Bronstein, A., Bronstein, M., Bustos, B., Darom, T., Horaud, R., Hotz, I., Keller, Y., Keustermans, J., & Kovnatsky, A., et al. (2011). SHREC 2011: Robust feature detection and description benchmark. In Eurographics Workshop on Shape Retrieval (pp. 79–86). Llandudno, UK.

  • Bro, R., Acar, E., & Kolda, T. (2008). Resolving the sign ambiguity in the singular value decomposition. Journal of Chemometrics, 22(2), 135–140.

    Article  Google Scholar 

  • Bronstein, A., Bronstein, M., Bustos, B., Castellani, U., Crisani, M., Falcidieno, B., Guibas, L., Kokkinos, I., Murino, V., & Ovsjanikov, M., et al. (2010a). SHREC 2010: Robust feature detection and description benchmark. In Eurographics Workshop on 3D Object Retrieval (pp. 320–322). Kerkrade, The Netherlands.

  • Bronstein, A., Bronstein, M., Castellani, U., Falcidieno, B., Fusiello, A., Godil, A., Guibas, L., Kokkinos, I., Lian, Z., & Ovsjanikov, M., et al. (2010b). SHREC 2010: Robust large-scale shape retrieval benchmark. In Eurographics Workshop on 3D Object Retrieval. Norrköping, Sweden.

  • Brown, M., & Lowe, D. (2003). Recognising panoramas. In 9th IEEE International Conference on Computer Vision (pp. 1218–1225). Nice, France.

  • Castellani, U., Cristani, M., Fantoni, S., & Murino, V. (2008). Sparse points matching by combining 3D mesh saliency with statistical descriptors. In S. Groeller (Ed.), In Computer Graphics Forum (pp. 643–652). Oxford: Blackwell.

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Curless, B., & Levoy, M. (1996). A volumetric method for building complex models from range images. In 23rd Annual Conference on Computer Graphics and Interactive, Techniques (pp. 303–312). New Orleans, LA.

  • Demi, M., Paterni, M., & Benassi, A. (2000). The first absolute central moment in low-level image processing. Computer Vision and Image Understanding, 80(1), 57–87.

    Article  MATH  Google Scholar 

  • Flint, A., Dick, A., & Hengel A. (2007). THRIFT: Local 3D structure recognition. In 9th Conference on Digital Image Computing Techniques and Applications (pp. 182–188).

  • Flint, A., Dick, A., & Van den Hengel, A. (2008). Local 3D structure recognition in range images. IET Computer Vision, 2(4), 208–217.

    Article  Google Scholar 

  • Frome, A., Huber, D., Kolluri, R., Bülow, T., & Malik, J. (2004). Recognizing objects in range data using regional point descriptors. In 8th European Conference on Computer Vision (pp. 224–237). Prague, Czech Republic.

  • Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., et al. (2003). A search engine for 3D models. ACM Transactions on Graphics, 22(1), 83–105.

    Article  Google Scholar 

  • Guennebaud, G., & Gross, M. (2007). Algebraic point set surfaces. ACM Transactions on Graphics, 26(3), 23.

    Article  Google Scholar 

  • Guo, Y., Bennamoun, M., Sohel, F., Wan, J., & Lu, M. (2013a). 3D free form object recognition using rotational projection statistics. In IEEE 14th Workshop on the Applications of Computer Vision (pp. 1–8). Clearwater, Florida.

  • Guo, Y., Sohel, F., Bennamoun, M., Wan, J., & Lu, M. (2013b). RoPS: A local feature descriptor for 3D rigid objects based on rotational projection statistics. In 1st International Conference on Communications, Signal Processing, and their Applications (pp. 1–6). Sharjah, UAE.

  • Guo, Y., Wan, J., Lu, M., & Niu, W. (2013c). A parts-based method for articulated target recognition in laser radar data. Optik. doi:http://dx.doi.org/10.1016/j.ijleo.2012.08.035.

  • Hetzel, G., Leibe, B., Levi, P., & Schiele, B. (2001). 3D object recognition from range images using local feature histograms. IEEE Conference on Computer Vision and Pattern Recognition, 2(II), 394.

    Google Scholar 

  • Hou, T., & Qin, H. (2010). Efficient computation of scale-space features for deformable shape correspondences. In European Conference on Computer Vision (pp. 384–397). Heraklion, Greece.

  • Hu, M. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(2), 179–187.

    Article  MATH  Google Scholar 

  • Johnson, A., & Hebert, M. (1998). Surface matching for object recognition in complex three-dimensional scenes. Image and Vision Computing, 16(9–10), 635–651.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ke, Y., & Sukthankar, R. (2004). PCA-SIFT: A more distinctive representation for local image descriptors. IEEE Conference on Computer Vision and Pattern Recognition, 2, 498–506.

    Google Scholar 

  • Kokkinos, I., Bronstein, M., Litman, R., & Bronstein, A. (2012). Intrinsic shape context descriptors for deformable shapes. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 159–166). Providence , RI.

  • Lei, Y., Bennamoun, M., & El-Sallam, A. (2013). An efficient 3D face recognition approach based on the fusion of novel local low-level features. Pattern Recognition, 46(1), 24–37.

    Article  Google Scholar 

  • Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  Google Scholar 

  • Malassiotis, S., & Strintzis, M. (2007). Snapshots: A novel local surface descriptor and matching algorithm for robust 3D surface alignment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(7), 1285–1290.

    Article  Google Scholar 

  • Mamic, G., & Bennamoun, M. (2002). Representation and recognition of 3D free-form objects. Digital Signal Processing, 12(1), 47–76.

    Article  Google Scholar 

  • Mian, A., Bennamoun, M., & Owens, R. (2006a). A novel representation and feature matching algorithm for automatic pairwise registration of range images. International Journal of Computer Vision, 66(1), 19–40.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Mikolajczyk, K., & Schmid, C. (2004). Scale & affine invariant interest point detectors. International Journal of Computer Vision, 60(1), 63–86.

    Article  Google Scholar 

  • Mikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), 1615–1630.

    Article  Google Scholar 

  • Novatnack, J., & Nishino, K. (2008). Scale-dependent/invariant local 3D shape descriptors for fully automatic registration of multiple sets of range images. In 10th European Conference on Computer Vision (pp. 440–453). Marseille, France.

  • Ohbuchi, R., Osada, K., Furuya, T., & Banno, T. (2008). Salient local visual features for shape-based 3D model retrieval. In IEEE International Conference on Shape Modeling and Applications (pp. 93–102).

  • Osada, R., Funkhouser, T., Chazelle, B., & Dobkin, D. (2002). Shape distributions. ACM Transactions on Graphics, 21(4), 807–832.

    Article  Google Scholar 

  • Paquet, E., Rioux, M., Murching, A., Naveen, T., & Tabatabai, A. (2000). Description of shape information for 2-D and 3-D objects. Signal Processing: Image Communication, 16(1), 103–122.

    Article  Google Scholar 

  • Petrelli, A., & Di Stefano, L. (2011). On the repeatability of the local reference frame for partial shape matching. In IEEE International Conference on Computer Vision (pp. 2244–2251). Barcelona, Spain.

  • Rodolà, E., Albarelli, A., Bergamasco, F., & Torsello, A. (2012). A scale independent selection process for 3D object recognition in cluttered scenes. International Journal of Computer Vision, 102, 129–145.

    Article  Google Scholar 

  • Shang, L., & Greenspan, M. (2010). Real-time object recognition in sparse range images using error surface embedding. International Journal of Computer Vision, 89(2), 211–228.

    Article  Google Scholar 

  • Shannon, C. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423.

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  • Sun, Y., & Abidi, M. (2001). Surface matching by 3D point’s fingerprint. In B. Buxton & R. Cipolla (Eds.), 8th IEEE International Conference on Computer Vision (pp. 263–269). Piscataway: Institute of Electrical and Electronics Engineers Inc.

    Google Scholar 

  • Taati, B., Bondy, M., Jasiobedzki, P., & Greenspan, M. (2007). Variable dimensional local shape descriptors for object recognition in range data. In 11th IEEE International Conference on Computer Vision (pp. 1–8). Rio de Janeiro, Brazil.

  • Taati, B., & Greenspan, M. (2011). Local shape descriptor selection for object recognition in range data. Computer Vision and Image Understanding, 115(5), 681–694.

    Google Scholar 

  • Tombari, F., Salti, S., & Di Stefano, L. (2010). Unique signatures of histograms for local surface description. In European Conference on Computer Vision (pp. 356–369). Crete, Greece.

  • Tombari, F., Salti, S., & Di Stefano, L. (2013). Performance evaluation of 3D keypoint detectors. International Journal of Computer Vision, 102, 198–220.

    Article  Google Scholar 

  • Yamany, S., & Farag, A. (2002). Surface signatures: An orientation independent free-form surface representation scheme for the purpose of objects registration and matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(8), 1105–1120.

    Article  Google Scholar 

  • Yamauchi, H., Saleem, W., Yoshizawa, S., Karni, Z., Belyaev, A., & Seidel, H. (2006). Towards stable and salient multi-view representation of 3D shapes. In IEEE International Conference on Shape Modeling and Applications (pp. 40–46). Matsushima, Japan.

  • Zaharescu, A., Boyer, E., & Horaud, R. (2012). Keypoints and local descriptors of scalar functions on 2D manifolds. International Journal of Computer Vision, 100, 78–98.

    Article  MATH  Google Scholar 

  • Zhong, Y. (2009). Intrinsic shape signatures: A shape descriptor for 3D object recognition. In IEEE International Conference on Computer Vision Workshops (pp. 689–696). Kyoto, Japan.

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Acknowledgments

The authors would like to acknowledge the following institutions. Stanford University for providing the 3D models; Bologna University for providing the 3D scenes; INRIA for providing the PHOTOMESH Dataset; Queen’s University for providing the 3D models and scenes; Università Ca’ Foscari Venezia for providing the 3D models and scenes. The authors also acknowledge A. Zaharescu from Aimetis Corporation for the results on the PHOTOMESH Dataset shown in Tables 3 and 4. This research is supported by a China Scholarship Council (CSC) scholarship and Australian Research Council Grants (DE120102960, DP110102166).

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Authors and Affiliations

  1. College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, People’s Republic of China

    Yulan Guo, Min Lu & Jianwei Wan

  2. School of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia

    Yulan Guo, Ferdous Sohel & Mohammed Bennamoun

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  1. Yulan Guo
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Correspondence to Yulan Guo.

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Guo, Y., Sohel, F., Bennamoun, M. et al. Rotational Projection Statistics for 3D Local Surface Description and Object Recognition. Int J Comput Vis 105, 63–86 (2013). https://doi.org/10.1007/s11263-013-0627-y

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