A point cloud classification and segmentation algorithm based on lightweight networks and weighted RF

doi:10.11996/JG.j.2095-302X.2026010143

Abstract

Abstract:

To address the issues of high computational cost and complex network models in point cloud classification and segmentation methods, a point cloud classification and segmentation algorithm based on lightweight networks and weighted Random Forest (RF) was proposed. The algorithm achieved efficient classification and segmentation in a hierarchical manner. Firstly, to address the issues of multiple layers and complex computation in traditional neural networks, a lightweight neural network was constructed to extract point cloud features such as global shape, inter-regional relationships, curvature, normal vector, and color, thereby achieving rapid rough classification and segmentation of point clouds. Then, to address data imbalance, an adaptive classification and segmentation strategy was designed. By introducing a weighted RF and combining inconsistency-measurement screening with dynamic-weighting optimization mechanisms, fine classification and segmentation of point clouds were achieved. The algorithm conducted classification experiments on the ModelNet40 dataset and segmentation experiments on the Semantic3D dataset and outdoor-scene point-cloud data. The results showed that compared with Local Geo-Transformer, PointNeXt, and FastPointNet++, classification and segmentation accuracy increased by approximately 1.9%, 1.6%, and 1.7%, respectively, while classification and segmentation time was reduced by approximately 40%, 30%, and 20%, respectively. Thus, the proposed point-cloud classification and segmentation algorithm based on lightweight networks and weighted RF can effectively reduce the training time of the model and improve the efficiency of classification and segmentation while maintaining high accuracy, making it an effective point cloud classification and segmentation algorithm.

Key words: point cloud classification and segmentation, lightweight network, weighted random forest, global shape, curvature

CLC Number:

ZHAO Fuqun, HAO Hanzhu, YU Jiale. A point cloud classification and segmentation algorithm based on lightweight networks and weighted RF[J]. Journal of Graphics, 2026, 47(1): 143-151.

Figures/Tables 10

References 24

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模型	OA/%	F1	IR/%
原始RF	85.30	83.9	─
静态特征权重优化RF	87.10	85.5	15.6
静态分类分割错误率优化RF	86.80	85.0	10.3
动态联合评估权重优化RF	89.96	88.1	32.8

模型	OA/%	F1	IR/%
原始RF	85.30	83.9	─
静态特征权重优化RF	87.10	85.5	15.6
静态分类分割错误率优化RF	86.80	85.0	10.3
动态联合评估权重优化RF	89.96	88.1	32.8

数据集	网络	参数量	OA/%	训练时间/min	单个物体识别时间/ms
ModelNet40	MVCNN^[20]	1.280 0×10⁸	91.10	500~800	8.40
	3DshapeNets^[21]	3.400 0×10⁷	78.52	1200	15.00
	VoxNet^[22]	8.900 0×10⁵	84.02	360~720	5.20
	PointNet^[23]	3.540 0×10⁶	88.53	254	0.82
	PointCNN^[24]	7.850 0×10⁵	89.32	600~800	1.50
	PointNet++^[7]	1.480 0×10⁶	90.50	663	11.23
	RG-GCN^[6]	2.200 0×10⁶	91.00	700~900	2.50
	Local Geo-Transformer^[9]	2.500 0×10⁵	92.50	193	0.54
	PointNeXt^[10]	4.200 0×10⁵	92.81	180	0.46
	FastPointNet++^[11]	2.500 0×10⁵	91.57	150	0.42
	本文算法	2.359 4×10⁵	93.23	143	0.39
Semantic3D	MVCNN^[20]	1.150 0×10⁸	83.80	550~750	7.20
	3DshapeNets^[22]	3.500 0×10⁷	77.50	1100	17.00
	VoxNet^[22]	1.200 0×10⁶	81.80	270	5.40
	PointNet^[23]	4.200 0×10⁶	86.50	200	3.00
	PointCNN^[24]	8.300 0×10⁵	88.50	375~525	5.80
	PointNet++^[7]	1.450 0×10⁶	89.90	640	8.30
	RG-GCN^[6]	1.400 0×10⁶	88.80	550~750	6.80
	Local Geo-Transformer^[9]	5.200 0×10⁵	90.10	90	1.40
	PointNeXt^[10]	6.500 0×10⁵	90.51	80	1.26
	FastPointNet++^[11]	3.800 0×10⁵	89.83	65	0.90
	本文算法	2.500 0×10⁵	91.80	55	0.70
室外场景点云数据	MVCNN^[20]	1.100 0×10¹¹	84.56	1700~2400	11.40
	3DshapeNets^[22]	3.500 0×10⁹	78.21	2900	21.20
	VoxNet^[22]	1.500 0×10⁹	79.34	900	7.56
	PointNet^[23]	3.000 0×10⁸	84.62	820	5.60
	PointCNN^[24]	8.520 0×10⁷	87.24	950~1200	8.12
	PointNet++^[7]	1.200 0×10⁹	88.27	1450	10.34
	RG-GCN^[6]	2.100 0×10⁸	88.43	1400~1600	8.23
	Local Geo-Transformer^[9]	3.318 3×10⁶	88.96	190	3.40
	PointNeXt^[10]	3.200 0×10⁶	89.11	145	2.30
	FastPointNet++^[11]	3.200 0×10⁶	89.56	164	2.90
	本文算法	3.012 3×10⁶	92.24	90	1.46

数据集	网络	参数量	OA/%	训练时间/min	单个物体识别时间/ms
ModelNet40	MVCNN^[20]	1.280 0×10⁸	91.10	500~800	8.40
	3DshapeNets^[21]	3.400 0×10⁷	78.52	1200	15.00
	VoxNet^[22]	8.900 0×10⁵	84.02	360~720	5.20
	PointNet^[23]	3.540 0×10⁶	88.53	254	0.82
	PointCNN^[24]	7.850 0×10⁵	89.32	600~800	1.50
	PointNet++^[7]	1.480 0×10⁶	90.50	663	11.23
	RG-GCN^[6]	2.200 0×10⁶	91.00	700~900	2.50
	Local Geo-Transformer^[9]	2.500 0×10⁵	92.50	193	0.54
	PointNeXt^[10]	4.200 0×10⁵	92.81	180	0.46
	FastPointNet++^[11]	2.500 0×10⁵	91.57	150	0.42
	本文算法	2.359 4×10⁵	93.23	143	0.39
Semantic3D	MVCNN^[20]	1.150 0×10⁸	83.80	550~750	7.20
	3DshapeNets^[22]	3.500 0×10⁷	77.50	1100	17.00
	VoxNet^[22]	1.200 0×10⁶	81.80	270	5.40
	PointNet^[23]	4.200 0×10⁶	86.50	200	3.00
	PointCNN^[24]	8.300 0×10⁵	88.50	375~525	5.80
	PointNet++^[7]	1.450 0×10⁶	89.90	640	8.30
	RG-GCN^[6]	1.400 0×10⁶	88.80	550~750	6.80
	Local Geo-Transformer^[9]	5.200 0×10⁵	90.10	90	1.40
	PointNeXt^[10]	6.500 0×10⁵	90.51	80	1.26
	FastPointNet++^[11]	3.800 0×10⁵	89.83	65	0.90
	本文算法	2.500 0×10⁵	91.80	55	0.70
室外场景点云数据	MVCNN^[20]	1.100 0×10¹¹	84.56	1700~2400	11.40
	3DshapeNets^[22]	3.500 0×10⁹	78.21	2900	21.20
	VoxNet^[22]	1.500 0×10⁹	79.34	900	7.56
	PointNet^[23]	3.000 0×10⁸	84.62	820	5.60
	PointCNN^[24]	8.520 0×10⁷	87.24	950~1200	8.12
	PointNet++^[7]	1.200 0×10⁹	88.27	1450	10.34
	RG-GCN^[6]	2.100 0×10⁸	88.43	1400~1600	8.23
	Local Geo-Transformer^[9]	3.318 3×10⁶	88.96	190	3.40
	PointNeXt^[10]	3.200 0×10⁶	89.11	145	2.30
	FastPointNet++^[11]	3.200 0×10⁶	89.56	164	2.90
	本文算法	3.012 3×10⁶	92.24	90	1.46