3D point cloud segmentation algorithm based on fused DenseNet and PointNet

Table 1. mIoU for 4 network architectures
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Table 1. mIoU for 4 network architectures
Method Train set Test set
PointNet 87.43 85.94
DSTN-PointNet 87.56 86.72
DMLP-PointNet 89.75 88.59
Ours 90.47 89.64

Table 2. Ablation experiment
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Table 2. Ablation experiment
Method Accuracy Recall F1 score
PointNet 0.80 0.75 0.77
+ DenseNet-STN and DenseNet-MLP 0.82 0.78 0.80
+ Add connection replacing Concat connection 0.85 0.81 0.83
+ THAM 0.88 0.85 0.86

Table 3. IoU for three segmentation networks

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Table 3. IoU for three segmentation networks

Method	mIoU	aero	cap	bag	chair	earphone	knife	table	rocket
PointNet	87.35	88.56	85.32	90.16	89.76	70.34	85.89	88.35	72.63
PointNet++	88.17	90.63	86.45	89.26	90.72	72.82	87.54	89.69	74.06
Ours	89.46	91.34	89.15	90.84	93.85	75.76	90.26	89.10	77.35
续表
Method	laptop	lamp	mug	motorbike	pistol	guitar	skateboard	car
PointNet	95.26	80.42	95.86	80.74	93.82	94.15	89.94	86.38
PointNet++	96.42	86.26	95.43	86.08	94.69	94.89	90.03	85.76
Ours	95.96	85.43	95.22	85.87	94.02	93.96	90.15	93.04

Table 4. Computational complexity of DenseNet-PointNet network in point cloud segmentation task
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Table 4. Computational complexity of DenseNet-PointNet network in point cloud segmentation task
Method Params/M FLOPs/M Train time/s Infer time/s
PointNet 3.53 445 0.52±0.01 0.03±0.00
PointNet++ 12.26 1 694 1.23±0.02 0.08±0.00
Ours 6.42 862 0.84±0.01 0.05±0.00

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Liequan WU, Zhifeng ZHOU, Yun SHI, Pulin REN. 3D point cloud segmentation algorithm based on fused DenseNet and PointNet[J]. Journal of Applied Optics, 2024, 45(5): 982

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