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Table 3 Comparison of classification rate on Washington RGB-D Objects database

From: 3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

Methods Classification accuracy in %
  RGB-D Only D
Random Forest [50] 79.600 ± 4.001 66.8 ± 2.5
Lai et al. [50] 81.9 ± 2.8 53.1 ± 1.7
Non-linear SVM 83.8 ± 3.5 64.7 ± 2.2
IDL [87] 85.4 ± 3.20 70.200 ± 2.001
CNN-RNN [88] 86.8 ± 3.3 78.9 ± 3.8
Bo et al. [89] 87.5 ± 2.9
Schwarz et al. [90] 89.4 ± 1.3
KPD2 + GKPD CD2 97.980 95.906a
Proposed method 98.486 97.274a
  1. aResult on depth data from a SwissRanger ToF camera