Point Cloud Analysis Method Based on Spatial Feature Attention Mechanism

Yanlin Qu; Yue Wang; Qian Zhang; Shaokun Han

doi:10.3788/LOP230840

Journals >Laser & Optoelectronics Progress >Volume 60 >Issue 24 >Page 2415003 > Article

Laser & Optoelectronics Progress
Vol. 60, Issue 24, 2415003 (2023)

Point Cloud Analysis Method Based on Spatial Feature Attention Mechanism

Yanlin Qu, Yue Wang, Qian Zhang, and Shaokun Han^*

Author Affiliations

Beijing Key Lab for Precision Optoelectronic Measurement Instrument and Technology, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China

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DOI: 10.3788/LOP230840 Cite this Article Set citation alerts

Yanlin Qu, Yue Wang, Qian Zhang, Shaokun Han. Point Cloud Analysis Method Based on Spatial Feature Attention Mechanism[J]. Laser & Optoelectronics Progress, 2023, 60(24): 2415003 Copy Citation Text

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Fig. 1. CSA module structure

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Fig. 2. CA module structure

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Fig. 3. SA module structure

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Fig. 4. Overview of the CSA-PointNet++ structure

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Fig. 5. Structure of the improved set abstraction module

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Fig. 6. Kinect V2 camera. (a) Appearance; (b) internal structure

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Fig. 7. Real-world self-constructed data. (a) Original point cloud data; (b) intercepted point cloud data

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Fig. 8. Point cloud data after translational transformation

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Fig. 9. Point cloud data after rotational transformation

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Fig. 10. Part segmentation results

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Method	ACC /%
MVCNN	90.10
VoxNet	85.90
PointNet	90.32
PointNet++	91.89
DGCNN	92.20
PCT	93.07
Ours（r=4，4）	93.20
Ours（r=4，16）	93.16
Ours（r=16，16）	93.10
Ours（r=16，4）	92.55
Ours（without ReLU）	92.56

Table 1. Classification results under ModelNet40 dataset (ACC)

Method	mIoU	airplane	bag	cap	car	chair	earphone	guitar	knife
PointNet	79.87	81.73	77.28	87.18	75.01	90.24	73.54	90.81	86.05
PointNet++	81.75	82.21	81.91	83.17	78.41	90.62	73.90	91.15	86.38
DGCNN	82.20	82.01	81.45	84.70	78.83	90.56	73.89	91.47	86.58
PCT	82.31
Ours	82.62	82.64	80.67	85.35	78.94	90.52	75.90	91.36	87.18
Method	lamp	laptop	motorbike	mug	pistol	rocket	skateboard	table
PointNet	81.85	95.15	61.27	93.26	79.98	48.17	74.22	82.25
PointNet++	83.06	95.64	70.91	95.87	80.65	55.19	76.84	82.04
DGCNN	83.81	95.70	70.37	94.97	81.15	61.53	76.56	81.65
PCT
Ours	84.08	95.63	71.33	95.43	82.06	61.91	76.61	82.29

Table 2. Part segmentation results under ShapeNetPart dataset (IoU)

Method	ACC /%
PointNet++	91.89
CA+PointNet++	92.93
SA+PointNet++	92.99
CSA+PointNet++	93.20
SCA+PointNet++	92.20

Table 3. ACC of the ablation experiment

Method	mIoU /%
PointNet++	81.75
CA+PointNet++	82.15
SA+PointNet++	82.42
CSA+PointNet++	82.62
SCA+PointNet++	82.13

Table 4. Part segmentation results of the ablation experiment (mIoU)

Type	ACC /%
With ground	52.81
Without ground	92.14

Table 5. ACC under the real-world self-constructed dataset

Translation	ACC /%
（x+1，y，z）	90.96
（x，y+1，z）	91.85
（x，y，z+1）	90.85
（x+1，y+1，z+1）	91.70

Table 6. ACC of the translated real-world self-constructed dataset

Rotation	ACC /%
Rot_30°	91.75
Rot_60°	89.59
Rot_90°	92.91
Rot_120°	90.99
Rot_150°	91.40
Rot_180°	90.59
Rot_210°	91.00
Rot_240°	90.01
Rot_270°	90.93
Rot_300°	91.37
Rot_330°	92.55

Table 7. ACC of the rotated real-world self-constructed dataset

Yanlin Qu, Yue Wang, Qian Zhang, Shaokun Han. Point Cloud Analysis Method Based on Spatial Feature Attention Mechanism[J]. Laser & Optoelectronics Progress, 2023, 60(24): 2415003

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