Evaluation of 3D Interest Point Detection Techniques via Human-generated Ground Truth

This benchmark aims to provide tools to evaluate 3D Interest Point Detection Algorithms with respect to human generated ground truth. Please refer to the paper for more information about this benchmark: Helin Dutagaci, Chun Pan Cheung, Afzal Godil: Evaluation of 3D interest point detection techniques via human-generated ground truth", The Visual Computer, 2012. 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. 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 next to the corresponding algorithm. Please refer to README for details. 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: Dutagaci, Helin, Chun Pan Cheung, and Afzal Godil. "Evaluation of 3D interest point detection techniques via human-generated ground truth." The Visual Computer 28.9 (2012): 901-917.