A Benchmark for 3D Interest Point Detection Algorithms

Description

This benchmark aims to provide tools to evaluate 3D Interest Point Detection Algorithms with respect to human generated ground truth. Using a web-based subjective experiment, human subjects marked 3D interest points on a set of 3D models. The models were organized in two datasets: Dataset A and Dataset B. Dataset A consists of 24 models which were hand-marked by 23 human subjects. Dataset B is larger with 43 models, and it contains all the models in Dataset B. The number of human subjects who marked all the models in this larger set is 16. Some of the models are standard models that are widely used in 3D shape research; and they have been used as test objects by researchers working on the best view problem. We have compared five 3D Interest Point Detection algorithms. The interest points detected on the 3D models of the dataset can be downloaded from the link below. Please refer to README for details in the download. Mesh saliency [Lee et al. 2005] : Interest points by mesh saliency Salient points [Castellani et al. 2008] : Interest points by salient points 3D-Harris [Sipiran and Bustos, 2010] : Interest points by 3D-Harris 3D-SIFT [Godil and Wagan, 2011] : Interest points by 3D-SIFT (Please note that some models in the dataset are not watertight, hence their volumetric representations could not be generated. Therefore, 3D-SIFT algorithm wasn't able to detect interest points for those models.) Scale-dependent corners [Novatnack and Nishino, 2007] : Interest points by SD corners HKS-based interest points [Sun et al. 2009] : Interest points by HKS method Please Cite the Paper: Helin Dutagaci, Chun Pan Cheung, Afzal Godil, ?Evaluation of 3D interest point detection techniques via human-generated ground truth?, The Visual Computer, 2012. References: [Lee et al. 2005] Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. In: ACM SIGGRAPH 2005, pp. 659?666 (2005) [Castellani et al. 2008] Castellani, U., Cristani, M., Fantoni, S., Murino, V.: Sparse points matching by combining 3D mesh saliency with statistical descriptors. Comput. Graph. Forum 27(2), 643?652 (2008) [Sipiran and Bustos, 2010] Sipiran, I., Bustos, B.: A robust 3D interest points detector based on Harris operator. In: Eurographics 2010 Workshop on 3D Object Retrieval (3DOR?10), pp. 7?14 (2010) [Godil and Wagan, 2011] Godil, A., Wagan, A.I.: Salient local 3D features for 3D shape retrieval. In: 3D Image Processing (3DIP) and Applications II, SPIE (2011) [Novatnack and Nishino, 2007] Novatnack, J., Nishino, K.: Scale-dependent 3D geometric features. In: ICCV, pp. 1?8, (2007) [Sun et al. 2009] Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. In: Eurographics Symposium on Geometry Processing (SGP), pp. 1383?1392 (2009)

Resources

Name Format Description Link
47 https://data.nist.gov/od/ds/mds2-2207/3DInterestPoint.zip.sha256
0 https://doi.org/10.18434/M32207
57 The zip file contains the 3D models for Dataset A and B in MAT files, 3D Interest Point Detection algorithms, Human subject interest points and the Evaluation code. Also the documentation is included. https://data.nist.gov/od/ds/mds2-2207/3DInterestPoint.zip

Tags

  • 3d-interest-points
  • evaluation-of-3d-interest-point
  • 3d-shape-analysis
  • d-salient-points

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