Semantic Segmentation of LiDAR Point Cloud Based on CAFF-PointNet

Ming Lai; Jiankang Zhao; Chuanqi Liu; Chao Cui; Haihui Long

doi:10.3788/LOP202158.2028004

Laser & Optoelectronics Progress, Volume. 58, Issue 20, 2028004(2021)

Semantic Segmentation of LiDAR Point Cloud Based on CAFF-PointNet

Ming Lai, Jiankang Zhao^*, Chuanqi Liu, Chao Cui, and Haihui Long

Department of Instrument Science & Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

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Figures & Tables(12)

Fig. 1. Overall framework of the model

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Fig. 2. Feature fusion module based on attention mechanism

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Fig. 3. Training and test sets (colored by height). (a) Training set; (b) test set

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Fig. 4. Loss function graph of training set and average accuracy graph of test set

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Fig. 5. Comparison of classification results between our model and ground truth. (a) Ground truth; (b) ours

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Fig. 6. Classification results of baseline and our model. (a) Ground truth ; (b) baseline; (c) ours

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Fig. 7. Comparison of classification results of different models. (a) Ground truth; (b) ours; (c) NANJ2; (d) RIT_1; (e) WhuY4

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Table 1. Calculation formulae for geometric features
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Table 1. Calculation formulae for geometric features
Geometric features L_λ P_λ S_λ O_λ E_λ A_λ Σ_λ C_λ
Formula $\frac{λ_{1} - λ_{2}}{λ_{1}}$ $\frac{λ_{2} - λ_{3}}{λ_{1}}$ $\frac{λ_{3}}{λ_{1}}$ $\sqrt[3]{λ_{1} λ_{2} λ_{3}}$ $\frac{λ_{1} - λ_{3}}{λ_{1}}$ - $\overset{3}{\sum_{i = 1}}$ λ_iln(λ_i) $\frac{λ_{3}}{λ_{1} + λ_{2} + λ_{3}}$

Table 2. The number of points in each category for the training and test sets

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Table 2. The number of points in each category for the training and test sets

Category	Training set	Test set
Powerline	546	600
Low vegetation	180850	98690
Impervious surfaces	193723	101986
Car	4614	3708
Fence/Hedge	12070	7422
Roof	152045	109048
Facade	27250	11224
Shrub	47605	24818
Tree	135173	54226
Total	753876	411722

Table 3. Confusion matrix of testing set classification results

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Table 3. Confusion matrix of testing set classification results

Category	Powerline	Low vegetation	Impervious surfaces	Car	Fence	Roof	Facade	Shrub	Tree
Powerline	371	2	1	0	0	156	59	0	11
Low vegetation	2	86137	8247	198	301	463	205	2764	373
Impervious surfaces	4	9139	92287	76	49	335	15	71	10
Car	1	168	234	2945	138	138	22	62	0
Fence	1	944	222	67	2188	112	153	3457	278
Roof	60	1310	99	22	23	102102	609	578	4245
Facade	61	1156	136	142	16	2288	6193	733	499
Shrub	7	6571	255	199	717	812	444	12308	3505
Tree	47	2394	23	17	139	1093	619	5060	44834
Precision /%	66.9	89.8	90.9	80.3	61.2	94.9	74.4	49.1	83.4
Recall /%	61.8	87.2	90.4	79.4	29.4	93.6	55.1	49.5	82.6
F₁ score /%	64.3	83.4	90.7	79.9	39.8	94.3	63.4	49.4	83.0

Table 4. Ablation experimental results of different models%

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Table 4. Ablation experimental results of different models%

Category	F₁ score
Category	Baseline	Ours(with GAM)	Ours(with AFF)	Ours(final)
Powerline	55.7	56.2	61.2	64.3
Low vegetation	80.7	80.8	82.1	83.4
Impervious surfaces	90.9	91.3	89.6	90.7
Car	77.8	73.6	75.2	79.9
Fence	30.5	28.4	34.5	39.8
Roof	92.5	94.5	93.8	94.3
Facade	56.9	55.3	60.3	63.4
Shrub	44.4	43.0	47.6	49.4
Tree	79.6	81.2	80.1	83.0
OA	82.2	82.6	83.2	84.8
Average of F₁	67.7	68.3	69.5	72.2

Table 5. Quantitative comparison between our model and other models%

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Table 5. Quantitative comparison between our model and other models%

Category	F₁ score
Category	RIT_1^[24]	WhuY4^[25]	NANJ2^[26]	PointNet++^[18]	PointSIFT^[19]	Ours
Powerline	37.5	42.5	62	57.9	55.7	64.3
Low vegetation	77.9	82.7	88.8	79.6	80.7	83.4
Impervious surfaces	91.5	91.4	91.2	90.6	90.9	90.7
Car	73.4	74.7	66.7	66.1	77.8	79.9
Fence	18	53.7	40.7	31.5	30.5	39.8
Roof	94	94.3	93.6	91.6	92.5	94.3
Facade	49.3	53.1	42.6	54.3	56.9	63.4
Shrub	45.9	47.9	55.9	41.6	44.4	49.4
Tree	82.5	82.8	82.6	77	79.6	83
OA	81.6	84.9	85.2	81.2	82.2	84.8
Average of F₁	63.3	69.2	69.3	65.6	67.7	72.2

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Ming Lai, Jiankang Zhao, Chuanqi Liu, Chao Cui, Haihui Long. Semantic Segmentation of LiDAR Point Cloud Based on CAFF-PointNet[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2028004

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