Point Cloud Analysis Combining Gated Self-Calibration Mechanism and Graphical Convolutional Network

Jiali Xu; Zhijun Fang; Shiqian Wu

doi:10.3788/LOP202259.1210017

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 12 >Page 1210017 > Article

Laser & Optoelectronics Progress
Vol. 59, Issue 12, 1210017 (2022)

Point Cloud Analysis Combining Gated Self-Calibration Mechanism and Graphical Convolutional Network

Jiali Xu¹, Zhijun Fang^1、*, and Shiqian Wu²

Author Affiliations

¹School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

²School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei , China

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DOI: 10.3788/LOP202259.1210017 Cite this Article Set citation alerts

Jiali Xu, Zhijun Fang, Shiqian Wu. Point Cloud Analysis Combining Gated Self-Calibration Mechanism and Graphical Convolutional Network[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1210017 Copy Citation Text

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Fig. 1. Structure of IE-Conv

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Fig. 2. Schematic diagrams of inner and outer point set module. (a) Internal point set module; (b) external point set module

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Fig. 3. Structure of internal point set module

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Fig. 4. Structure of external point set module

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Fig. 5. Architecture of interior-exterior point set shape feature convolutional network

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Method	Input	Points /10³	Acc /%
Pointwise-CNN^［31］	pnt	1	86.1
ECC^［19］	pnt	1	87.4
PointNet^［8］	pnt	1	89.2
Point-CNN^［33］	pnt	1	91.7
DGCNN^［10］	pnt	1	92.2
SO-CNN^［15］	pnt	1	93.1
Dense-Point^［26］	pnt	1	93.2
RS-CNN^［14］	nor	1	92.8
PAT^［27］	pnt，nor	1	91.7
Spec-GCN^［30］	pnt，nor	1	91.8
PointConv^［1］	pnt，nor	1	92.5
A-CNN^［11］	pnt，nor	1	92.6
PointASNL^［13］	pnt，nor	1	93.2
ELM^［28］	pnt，nor	1	93.2
RS-CNN^［14］	pnt，nor	1	93.6
SO-Net^［29］	pnt，nor	2	90.9
PointNet++^［9］	pnt，nor	5	91.9
Spider-CNN^［32］	pnt，nor	5	92.4
SO-Net^［29］	pnt，nor	5	93.4
Proposed method	pnt，nor	1	93.9

Table 1. Comparison of classification accuracy for ModelNet40 dataset

Method	Number of parameters /MB	Acc /%	FLOPs /sample
PointNet	3.50	89.2	440
Spec-GCN	2.05	91.8	1112
PointNet++	1.48	91.9	1684
DGCNN	1.84	92.2	2767
Proposed method	1.37	93.9	266

Table 2. Comparison of classification complexity and time of the ModelNet40 dataset

Method	Air	Bag	Cap	Car	Chai	Ear.	Gui.	Knife	Lamp	Lap	Moto	Mug	Pistol	Rock	Skate	Table	Mean
PointNet^［8］	83.4	78.7	82.5	74.9	89.6	73.0	91.5	85.9	80.8	95.3	65.2	93.0	81.2	57.9	72.8	80.6	83.7
SONet^［29］	82.8	77.8	88.0	77.3	90.6	73.5	90.7	83.9	82.8	94.8	69.1	94.2	80.9	53.1	72.9	83.0	84.9
PointNet++^［9］	82.4	79.0	87.7	77.3	90.8	71.8	91.0	85.9	83.7	95.3	71.6	94.1	81.3	58.7	76.4	82.6	85.1
DGCNN^［10］	84.2	83.7	84.4	77.1	90.9	78.5	91.5	87.3	82.9	96.0	67.8	93.3	82.6	59.7	75.5	80.6	85.1
PCNN^［1］	82.4	80.1	85.5	79.5	90.8	73.2	91.3	86.0	85.0	95.7	73.2	94.8	83.3	51.0	75.0	81.8	85.1
ELM^［28］	84.0	80.4	88.0	80.2	90.7	77.5	91.2	86.4	82.6	95.5	70.0	93.9	84.1	55.6	75.6	82.1	85.3
SpiderCNN^［32］	83.5	81.0	87.2	77.5	90.7	76.8	91.1	87.3	83.3	95.8	70.2	93.5	82.7	59.7	75.8	82.8	85.3
SO-CNN	83.9	84.1	85.0	77.4	91.3	78.3	91.7	87.4	83.8	96.4	69.7	93.5	83.1	58.9	76.2	82.9	85.7
A-CNN^［11］	84.2	84.0	88.0	79.6	91.3	75.2	91.6	87.1	85.5	95.4	75.3	94.9	82.5	67.8	77.5	83.3	86.1
RS-CNN^［14］	83.5	84.8	88.8	79.6	91.2	81.1	91.6	88.4	86.0	96.0	73.7	94.1	83.4	60.5	77.7	83.6	86.2
Proposed method	84.0	86.2	88.1	79.5	91.6	77.5	91.3	88.0	86.3	96.1	72.8	95.0	83.6	62.2	75.9	83.9	86.4

Table 3. Comparison of segmentation accuracy of ShapeNet dataset

Object	Visualization		Object	Visualization
Air			Lamp
Bag			Laptop
Cap			Motor.
Car			Mug
Chair			Pistol
Ear.			Rocket
Gui.			Skate
Knife			Table

Table 4. Visualization results of SR-Net) on ShapeNet dataset

Model	RS-CNN（*）	Internal	External	Internal-external	Acc /%
A					90.1
B	√				92.0
C		√			93.2
D	√		√		92.9
E				√	93.9

Table 5. Ablation experiments with modules on ModelNet40 dataset

Model	Priori expressions	Channel	Acc /%
A	${‖p_{i} - p_{i, j}‖}_{e d}$	1	92.4
A	${‖p_{i} - p_{i, j}‖}_{c o s d}$	1	92.4
B	$p_{i}^{c o o r d}$ 、 $p_{i, j}^{c o o r d}$	6	93.4
B	$p_{i}^{n o r}$ 、 $p_{i, j}^{n o r}$	6	93.4
C	$p_{i}^{c o o r d}$ 、 $p_{i, j}^{c o o r d}$ 、 ${‖p_{i} - p_{i, j}‖}_{e d}$	7	93.9
C	$p_{i}^{n o r}$ 、 $p_{i, j}^{n o r}$ 、 ${‖p_{i} - p_{i, j}‖}_{c o s d}$	7	93.9
D	$p_{i}^{c o o r d}$ 、 $p_{i, j}^{c o o r d}$ 、 ${‖p_{i} - p_{i, j}‖}_{e d}$	7	93.0
E	$p_{i}^{n o r}$ 、 $p_{i, j}^{n o r}$ 、 ${‖p_{i} - p_{i, j}‖}_{c o s d}$	7	92.5

Table 6. Ablation experiments on ModelNet40 dataset for different prior expressions as gates

Method	Self-calibrate	Translation		Rotate
Method	Self-calibrate	-0.2	+0.2	90˚	180˚
PointNet		70.8	70.6	42.5	38.6
PointNet++		88.2	88.2	47.9	39.7
Proposed method		90.9	90.9	90.9	90.9
Proposed method	√	92.1	92.1	92.1	92.1

Table 7. Robustness experiments on ModelNet40 dataset after adding translations or rotations to point clouds

Model	Aggregation function	Self-calibrate	Acc /%
A	Avg		92.7
B	Max		93.2
C	Max	√	93.5
D	Sum		93.5
E	Sum	√	93.9

Table 8. Ablation experiments of different aggregation functions and self calibration functions on ModelNet40 dataset

Jiali Xu, Zhijun Fang, Shiqian Wu. Point Cloud Analysis Combining Gated Self-Calibration Mechanism and Graphical Convolutional Network[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1210017

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