Lightweight Template Matching Algorithm Based on Rendering Perspective Sampling

Table 1. Structure of proposed model
View table
Table 1. Structure of proposed model
Block name Input channel Output channel Kernel size Expand channel
Conv 3 32 3 –
BaseBlock 32 16 3 –
BaseBlock 16 24 3 96
BaseBlock 24 24 3 144
BaseBlock 24 40 5 144
BaseBlock 40 40 5 240
Conv 40 16 1 –

Table 2. Comparison of model parameter quantity
View table
Table 2. Comparison of model parameter quantity
Network Running time /min Inference time /ms Number of parameters /10⁶ Memory size /MB
BaseNet^［25-26］ 0.31 5.68 0.024 0.048
ResNet^［23］ 1.15 1126.20 24.036 92.053
Proposed 0.74 48.37 0.043 20.464

Table 3. Details of datasets
View table
Table 3. Details of datasets
Split No. Train Test Unseen object name
1 9954 15038 Ape，benchvise，camera，can
2 9928 15064 Cat，driller，duck，eggbox
3 8850 16142 Glue，holepuncher，iron，lamp，phone

Table 4. Comparison of Acc15 on LM and O-LM datasets

View table

Table 4. Comparison of Acc15 on LM and O-LM datasets

Method	Render	Seen LM	Seen O-LM	Unseen LM	Unseen O-LM
Wohlhart’s^［25］	Blender	95.2	19.6	13.3	8.2
Balntas’s^［26］	Blender	96.3	18.3	11.5	7.1
Template-pose^［23］	Blender	99.3	77.3	94.4	71.4
Template-pose^［23］	PyTorch3D	98.5	75.7	95.7	70.3
Proposed	PyTorch3D	97.6	80.0	98.1	74.8

Table 5. Repeated experiments with LM and O-LM datasets

View table

Table 5. Repeated experiments with LM and O-LM datasets

Split No.	Method	Render	Seen LM	Seen O-LM	Unseen LM	Unseen O-LM	Average
2	Template-pose	Blender	99.0	84.1	97.4	72.7	88.3
2	Template-pose	PyTorch3D	97.0	77.9	96.6	70.5	85.5
2	Proposed	PyTorch3D	96.8	81.9	98.8	76.5	88.5
3	Template-pose	Blender	99.2	76.8	88.7	85.3	87.5
3	Template-pose	PyTorch3D	99.0	75.2	85.2	78.3	84.4
3	Proposed	PyTorch3D	99.2	78.4	88.3	88.3	88.6

Table 6. Comparison of iterative optimization
View table
Table 6. Comparison of iterative optimization
Condition Seen LM Seen O-LM Unseen LM Unseen O-LM
Without iteration 4.70 11.45 5.57 20.94
With iteration 4.16 11.18 4.74 20.47

Table 7. Attention mechanism ablation experiment
View table
Table 7. Attention mechanism ablation experiment
Condition Seen LM Seen O-LM Unseen LM Unseen O-LM Average
With SE 99.2 78.4 88.3 88.3 88.6
Without SE 99.1 77.2 86.6 86.8 87.4

Table 8. Deep separable convolutional ablation experiment
View table
Table 8. Deep separable convolutional ablation experiment
Condition Seen LM /% Seen O-LM /% Unseen LM /% Unseen O-LM /% Average /% Inference time /ms
With DW 99.2 78.4 88.3 88.3 88.6 48.37
Without
DW
97.4 70.0 62.5 43.6 68.4 669.25

Tools

Get Citation

Copy Citation Text

Daizhou Wen, Xi Wang, Mingjun Ren. Lightweight Template Matching Algorithm Based on Rendering Perspective Sampling[J]. Laser & Optoelectronics Progress, 2024, 61(18): 1837009

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites