EURASIP Journal on Advances in Signal Processing

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	95.906^a
Proposed method	98.486	97.274^a

^aResult on depth data from a SwissRanger ToF camera

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