Point cloud change detection with geometric structure refinement and entropy-attention mechanism

doi:10.11947/j.AGCS.2026.20250415

Abstract

Abstract:

The aim of three-dimensional change detection technology is to identify temporal feature alterations from multi-temporal point clouds within identical geographical scenes, which plays a crucial role in understanding surface dynamics. Conversely, contemporary deep-learning-based approaches always underperform in complex urban settings characterized by non-uniform point density, occlusions, and multi-viewpoint overlaps, which arises from insufficient incorporation of local geometric intricacies and global contextual dependencies. Therefore, we propose GS-EANet (geometric structure refinement and entropy-attention network) which effectively models scene-aware geometric complexity for robust 3D change detection. During the encoding stage, GS-EANet combines local complexity analysis module and stratified sampling module, which uses three key features including point curvature, density, and information entropy, to describe the spatial arrangement of points in a neighborhood. This multi-feature fusion serves as prior knowledge for calculating sampling probabilities. The stratified sampling module based on sampling probability notably optimizes geometric structures while down-sampling, thereby achieving an optimal balance between global scene coverage and emphasis on critical regions. Furthermore, we advance conventional dual-path attention mechanisms by incorporating information-entropy, which dynamically steers spatial and channel attention toward structurally complex regions, substantially enhancing global contextual representation. Comprehensive evaluations on the SLPCCD and Urb3DCD datasets demonstrate that GS-EANet surpasses seven state-of-the-art baseline methods in both quantitative and qualitative assessments. Notably, on the SLPCCD dataset, GS-EANet achieves a mean intersection over union (mIoU) of 17.01% higher than 3DCDNet. Moreover, GS-EANet outperforms the previous best-performing model, Ms-DANet, by 3.35%. The proposed approach effectively mitigates false or missed detections caused by density variations, occlusions, and viewpoint overlaps, establishing a new state-of-the-art in 3D change detection for complex urban environment.

Key words: 3D point cloud, change detection, geometric structure optimization, entropy attention, dual-path attention mechanism

CLC Number:

P237

Han Zhu, Chenguang Dai, Zhenchao Zhang, Xuanguang Liu, Jinhao Lu. Point cloud change detection with geometric structure refinement and entropy-attention mechanism[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(8): 1439-1451.

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References 44

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模型	IoU			OA	mIoU
模型	移除	增加	不变	OA	mIoU
PointNet^[40]*	23.47	26.84	89.36	89.16	46.56
GCN^[41]*	28.23	35.85	88.61	89.80	50.90
PointNet++^[42]*	45.36	57.21	95.31	96.03	65.96
PointMLP^[43]*	46.83	40.11	90.35	92.56	59.10
3DCDNet^[27-28]	52.82	58.23	93.87	93.51	68.31
PGN3DCD^[30]	74.58	75.25	96.67	96.97	82.17
Ms-DANet^[31]	73.54	75.50	96.88	97.14	81.97
GS-EANet	79.84	78.14	97.98	97.56	85.32

模型	FLOPs/G	Params/M	推理速度/（帧/s）	mIoU/（%）
3DCDNet^[27-28]	88.20	6.68	53.29	68.31
Ms-DANet^[31]	106.31	6.37	44.73	81.97
GS-EANet	93.41	6.45	48.80	85.32

网络	LCA	SSM	DPA	EG-DPA	IoU			OA	mIoU
网络	LCA	SSM	DPA	EG-DPA	移除	增加	不变	OA	mIoU
3DCDNet^[27-28]	×	×	×	×	52.82	58.23	93.87	93.51	68.31
GS-EANet-1	√	×	×	×	55.98	60.21	94.30	94.03	70.16
GS-EANet-2	×	×	√	×	58.69	62.06	93.99	93.74	71.58
GS-EANet-3	×	×	×	√	63.96	64.87	95.53	94.79	74.79
GS-EANet-4	√	√	×	×	60.32	62.98	95.02	95.04	72.77
GS-EANet-5	√	√	√	×	74.08	75.14	97.11	96.82	82.11
GS-EANet	√	√	×	√	79.84	78.14	97.98	97.56	85.32

数据集	网络	IoU			OA	mIoU
数据集	网络	移除	增加	不变	OA	mIoU
子数据集1-b ALS Low Res	3DCDNet^[27-28]	37.62	45.84	98.63	96.21	60.70
	Ms-DANet^[31]	53.10	62.45	98.55	98.57	71.37
	GS-EANet	56.12	65.09	98.64	98.37	73.28
子数据集1-a ALS Low Res	3DCDNet^[27-28]	48.31	62.17	98.03	96.58	69.50
	Ms-DANet^[31]	63.86	78.67	99.04	99.06	80.52
	GS-EANet	66.21	78.22	99.65	98.89	81.36
子数据集1-c ALS Low Res	3DCDNet^[27-28]	55.03	62.31	99.08	98.14	72.14
	Ms-DANet^[31]	66.85	83.49	99.20	99.22	83.18
	GS-EANet	70.66	86.21	99.05	98.98	85.31
子数据集2 ALS High Res	3DCDNet^[27-28]	55.63	70.59	99.03	98.67	75.08
	Ms-DANet^[31]	67.74	76.59	99.06	99.08	81.13
	GS-EANet	70.36	80.21	99.23	99.21	83.27
子数据集3 ALS High Noise	3DCDNet^[27-28]	40.31	58.69	99.02	98.32	65.80
	Ms-DANet^[31]	55.27	72.15	98.92	98.93	75.45
	GS-EANet	65.04	69.89	99.23	99.05	78.05
子数据集4 Photogrammetry	3DCDNet^[27-28]	51.23	65.21	98.06	98.25	71.74
	Ms-DANet^[31]	61.42	76.95	99.15	99.17	79.17
	GS-EANet	67.32	78.21	99.06	99.12	81.53
子数据集5 Multi-Sensor	3DCDNet^[27-28]	45.63	62.58	98.12	98.62	68.78
	Ms-DANet^[31]	54.84	71.96	98.68	98.71	75.16
	GS-EANet	59.98	76.36	99.06	99.12	78.47

数据集	Ms-DANet的mIoU/（%）	GS-EANet		跨区域迁移试验后的mIoU/（%）	跨区域迁移精度变化情况/（%）
数据集	Ms-DANet的mIoU/（%）	最优权重系数组合（ω_C，ω_H，ω_ρ）	GS-EANet最优mIoU/（%）	跨区域迁移试验后的mIoU/（%）	跨区域迁移精度变化情况/（%）
Urb3DCD 1-a	71.37	（0.4，0.4，0.2）	73.28	73.06	↓0.22
Urb3DCD 1-b	80.52	（0.4，0.4，0.2）	81.36	81.22	↓0.14
Urb3DCD 1-c	83.18	（0.4，0.4，0.2）	85.31	85.08	↓0.23
Urb3DCD 2	81.13	（0.3，0.5，0.2）	83.27	82.89	↓0.38
Urb3DCD 3	75.45	（0.2，0.5，0.3）	78.05	77.53	↓0.52
Urb3DCD 4	79.17	（0.3，0.3，0.4）	81.53	81.39	↓0.14
Urb3DCD 5	75.16	（0.3，0.2，0.5）	78.47	78.15	↓0.33