Object Pose Estimation Method Based on Keypoint Distance Network

Object	PoseCNN	PVNet	DenseFusion	G2L-Net	Proposed method
mean	88.6	86.3	94.3	98.7	99.8
ape	77.0	43.6	92.3	96.8	98.7
benchvise	97.5	99.9	93.2	96.1	100.0
camera	93.5	86.9	94.4	98.2	99.9
can	96.5	95.5	93.1	98.0	100.0
cat	82.1	79.3	96.5	99.2	99.9
driller	95.0	96.4	87.0	99.8	100.0
duck	77.7	52.6	92.3	97.7	98.6
eggbox	97.1	99.2	99.8	100.0	100.0
glue	99.4	95.7	100.0	100.0	99.9
holepuncher	52.8	82.0	92.1	99.0	100.0
iron	98.3	98.9	97.0	99.3	100.0
lamp	97.5	99.3	95.3	99.5	100.0
phone	87.7	92.4	92.8	98.9	100.0

Table 2. Test results on YCB-Video dataset [AUC-ADD(S)]

View table

Table 2. Test results on YCB-Video dataset [AUC-ADD(S)]

Object	PoseCNN		DenseFusion		GDR-Net		Proposed method
Object	ADD-S	ADD（S）	ADD-S	ADD（S）	ADD-S	ADD（S）	ADD-S	ADD（S）
mean	75.9	59.9	91.2	82.9	91.6	84.3	94.1	90.1
02 master chef can	83.9	50.2	95.3	70.7	96.3	65.2	95.1	77.7
03 cracker box	76.9	53.1	92.5	86.9	97.0	88.8	93.9	89.4
04 sugar box	84.2	68.4	95.1	90.8	98.9	95.0	96.5	94.4
05 tomato soup can	81.0	66.2	93.8	84.7	96.5	91.9	94.9	89.2
06 mustard bottle	90.4	81.0	95.8	90.9	100.0	92.8	96.8	94.7
07 tuna fish can	88.0	70.7	95.7	79.6	99.4	94.2	95.0	90.3
08 pudding box	79.1	62.7	94.3	89.3	64.6	44.7	93.9	87.4
09 gelatin box	87.2	75.2	97.2	95.8	97.1	92.5	97.0	94.8
10 potted meat can	78.5	59.5	89.3	79.6	86.0	80.2	89.9	81.8
11 banana	86.0	72.3	90.0	76.7	96.3	85.8	95.2	91.1
19 pitcher base	77.0	53.3	93.6	87.1	99.9	98.5	96.6	94.9
21 bleach cleanser	71.6	50.3	94.4	87.5	94.2	84.3	95.0	90.6
24 bowl	69.6	69.6	86.0	86.0	85.7	85.7	88.1	88.1
25 mug	78.2	58.5	95.3	83.8	99.6	94.0	96.8	92.5
35 power drill	72.7	55.3	92.1	83.7	97.5	90.1	95.6	92.7
36 wood block	64.3	64.3	89.5	89.5	82.5	82.5	90.5	90.5
37 scissors	56.9	35.8	90.1	77.4	63.8	49.5	93.6	89.9
40 large marker	71.7	58.3	95.1	89.1	88.0	76.1	95.0	85.3
51 large clamp	50.2	50.2	71.5	71.5	89.3	89.3	93.3	93.3
52 extra large clamp	44.1	44.1	70.2	70.2	93.5	93.5	88.2	88.2
61 foam brick	88.0	88.0	92.2	92.2	96.9	96.9	95.8	95.8

Table 3. Accuracy comparison of different backbone network modules
View table
Table 3. Accuracy comparison of different backbone network modules
Dataset ResNet Proposed module
LineMOD 99.6 99.8
YCB-Video，ADD-S 92.1 94.1
YCB-Video，ADD（S） 86.5 90.1

Table 4. Feature network parameters of different backbone network modules
View table
Table 4. Feature network parameters of different backbone network modules
Module ResNet Proposed module
Parameters /MB 33.7 15.4

Table 5. Comparison of results with or without data processing
View table
Table 5. Comparison of results with or without data processing
Dataset Without data processing With data processing
LineMOD 96.1 96.6
YCB-Video，ADD-S 93.5 94.1
YCB-Video，ADD（S） 88.3 90.1

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Meng Xia, Hongzhi Du, Jiarui Lin, Yanbiao Sun, Jigui Zhu. Object Pose Estimation Method Based on Keypoint Distance Network[J]. Laser & Optoelectronics Progress, 2023, 60(16): 1615008

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