Combining 3D Shape and Color for 3D Object Recognition

  • Susana BrandãoEmail author
  • João P. Costeira
  • Manuela Veloso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9730)


We present new results in object recognition based on color and 3D shape obtained from 3D cameras. Namely, we further exploit diffusion processes to represent shape and the use of color/texture as a perturbation to the diffusion process. Diffusion processes are an effective tool to replace shortest path distances in the characterization of 3D shapes. They also provide effective means for the seamlessly representation of color and shape, mainly because they provide information both the color and on their distribution over surfaces. While there have been different approaches for incorporating color information in the diffusion process, this is the first work that explores different parameterizations of color and their impact on recognition tasks. We present results using very challenging datasets, where we propose to recognize different instances of the same object class assuming a very limited a-priori knowledge on each individual object.


Heat Kernel Library Size Partial View Precision Result Instant Noodle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Abdelrahman, M., Farag, A., Swanson, D., El-Melegy, M.: Heat diffusion over weighted manifolds: A new descriptor for textured 3D non-rigid shapes. In: CVPR (2015)Google Scholar
  2. 2.
    Blum, M., Springenberg, J.T., Wülfing, J., Riedmiller, R.: A learned feature descriptor for object recognition in RGB-D data. In: ICRA (2012)Google Scholar
  3. 3.
    Brandão, S., Costeira, J.P., Veloso, M.V.: The partial view heat kernel descriptor for 3D object representation. In: ICRA (2014)Google Scholar
  4. 4.
    Brandão, S., Veloso, M., Costeira, J.P.: Multiple hypotheses for object class disambiguation from multiple observations. In: 2nd International Conference on 3D Vision, 3DV 2014, Tokyo, Japan, December 8–11, 2014 (2014)Google Scholar
  5. 5.
    Kovnatsky, A., Bronstein, M.M., Bronstein, A.M., Kimmel, R.: Photometric heat kernel signaturesGoogle Scholar
  6. 6.
    Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view RGB-D object dataset. In: ICRA, May 2011Google Scholar
  7. 7.
    Lai, K., Bo, L., Ren, X., Fox, D.: Sparse distance learning for object recognition combining RGB and depth information. In: ICRA (2011)Google Scholar
  8. 8.
    Ribeiro, F., Brandão, S., Costeira, J.P., Veloso, M.: Global localization by soft object recognition from 3D partial views. In: IROS (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Susana Brandão
    • 1
    Email author
  • João P. Costeira
    • 1
  • Manuela Veloso
    • 2
  1. 1.Instituto Superior TécnicoLisboaPortugal
  2. 2.Carnegie Mellon UniversityPittsburghUSA

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