DGOA：基于动态图和偏移注意力的点云上采样

doi:10.11996/JG.j.2095-302X.2024010219

图学学报 ›› 2024, Vol. 45 ›› Issue (1): 219-229.DOI: 10.11996/JG.j.2095-302X.2024010219

• 计算机图形学与虚拟现实 • 上一篇下一篇

DGOA：基于动态图和偏移注意力的点云上采样

韩亚振¹(), 尹梦晓¹^,²(), 马伟钊¹, 杨诗耕¹, 胡锦飞¹, 朱丛洋¹

1.广西大学计算机与电子信息学院，广西南宁 530004
2.广西多媒体通信与网络技术重点实验室，广西南宁 530004

收稿日期:2023-06-29 接受日期:2023-10-27 出版日期:2024-02-29 发布日期:2024-02-29
通讯作者:尹梦晓(1978-)，女，副教授，博士。主要研究方向为计算机图形学、数字几何处理。E-mail：ymx@gxu.edu.cn
第一作者:韩亚振(1997-)，男，硕士研究生。主要研究方向为点云处理。E-mail：2013301011@st.gxu.edu.cn
基金资助:
国家自然科学基金项目(61762007)

DGOA: point cloud upsampling based on dynamic graph and offset attention

HAN Yazhen¹(), YIN Mengxiao¹^,²(), MA Weizhao¹, YANG Shigeng¹, HU Jinfei¹, ZHU Congyang¹

1. School of Computer and Electronics Information, Guangxi University, Nanning Guangxi 530004, China
2. Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning Guangxi 530004, China

Received:2023-06-29 Accepted:2023-10-27 Published:2024-02-29 Online:2024-02-29
First author：HAN Yazhen (1997-), master student. His main research interest covers point cloud processing. E-mail：2013301011@st.gxu.edu.cn
Supported by:
National Natural Science Foundation of China(61762007)

摘要/Abstract

摘要：

由三维扫描设备直接得到的点云经常是稀疏、不均匀、有噪声的，因而点云上采样在点云重建、渲染等领域扮演了越来越关键的角色。为此提出了一种新的基于动态图和偏移注意力的点云上采样网络DGOA，主要包含局部特征提取(LFE)、全局特征提取(GFE)和坐标重建(CR) 3个模块。LFE采用多层结构提取邻域信息，每层基于特征相似性构建动态图，可以在特征空间自适应的将点云分组，增大感受野，获得长距离的语义信息，更好的建模点云的局部几何形状。GFE采用基于拉普拉斯算子的偏移注意力使每个点都能获得点云的全局信息，使生成点云的细节与原始点云一致，减少噪声的影响。CR借鉴FoldingNet操作，避免生成点的聚集。此外，整个网络与输入点云中点的顺序无关，具有置换不变性。在多个数据集的定量与定性实验结果表明，该方法优于其他方法，并且具有良好的泛化性和稳定性。

广西大学尹梦晓副教授及其学生韩亚振等提出一种针对点云的上采样算法DGOA。采用多层结构提取邻域信息，每层基于特征相似性构建动态图，更好的建模点云的局部形状。采用基于拉普拉斯算子的偏移注意力使每个点都能获得点云的全局信息，使生成点云的细节与原始点云一致，减少噪声影响。整个网络具有置换不变性。多个数据集上的实验结果表明本文方法具有良好的有效性和稳定性。

关键词: 点云, 点云上采样, 动态图, 偏移注意力, 深度学习

Abstract:

The point clouds obtained directly from 3D scanning equipment are often sparse, uneven, and noisy. Therefore, point cloud upsampling has become increasingly vital in fields such as point cloud reconstruction and rendering. A new point cloud upsampling network named DGOA was proposed based on Dynamic Graph and Offset Attention. DGOA mainly consisted of three modules: LFE (local feature extraction), GFE (global feature extraction), and CR (coordinate reconstruction). LFE utilized a multi-layer structure to extract neighborhood information, constructed a dynamic graph based on feature similarity at each layer, and adaptively grouped point clouds in the feature space. This increased the receptive field, obtained long-distance semantic information, and more effectively modeled the local geometry of the point cloud. GFE employed offset attention based on the Laplace operator, enabling each point to obtain global information of the point cloud. This ensured that the details of the generated point cloud were consistent with the original point cloud and reduced the impact of noise. CR, inspired by the FoldingNet operation, prevented the generated points from clustering together. In addition, the entire network was permutation invariant with respect to the order of points in the input point cloud. Quantitative and qualitative experimental results on multiple datasets demonstrated that the proposed method outperformed other methods and exhibited good generalization and stability.

Key words: point cloud, point cloud upsampling, dynamic graph, offset attention, deep learning

中图分类号:

TP391

韩亚振, 尹梦晓, 马伟钊, 杨诗耕, 胡锦飞, 朱丛洋. DGOA：基于动态图和偏移注意力的点云上采样[J]. 图学学报, 2024, 45(1): 219-229.

HAN Yazhen, YIN Mengxiao, MA Weizhao, YANG Shigeng, HU Jinfei, ZHU Congyang. DGOA: point cloud upsampling based on dynamic graph and offset attention[J]. Journal of Graphics, 2024, 45(1): 219-229.

图/表 16

图1 DGOA整体结构图，包含局部特征提取、全局特征提取、坐标重建3个模块

Fig. 1 DGOA architecture, including three modules: local feature extraction (LFE), global feature extraction (GFE), and coordinate reconstruction (CR)

图2 动态图的具体操作，通过该操作获取局部信息

Fig. 2 Details of Dynamic Graph, obtaining local information through this operation

图3 在特征空间自适应的构造动态图来扩大感受野

Fig. 3 Adaptively constructing a dynamic graph in the feature space to expand the receptive field

图4 偏移注意力的具体操作，使每个点都具有全局感受野

Fig. 4 Details of offset attention, enabling every point to have a global receptive field

图5 坐标重建模块的具体操作，将点云从特征空间映射回坐标空间

Fig. 5 Details of coordinate reconstruction, mapping the point cloud back from the feature space to the coordinate space

表1 在PU-GAN数据集上的定量比较

Table 1 Quantitative results on PU-GAN dataset

网络	CD↓	HD↓	EMD↓	P2F(avg)↓	P2F(std)↓
PU-Net^[3]	0.556	4.750	40.146	4.678	5.946
MPU^[10]	0.298	4.700	30.534	2.855	5.180
PU-GAN^[11]	0.280	4.640	26.243	2.330	4.431
PU-GCN^[12]	0.258	1.885	24.460	2.721	3.542
Dis-PU^[15]	0.260	2.104	25.312	2.480	3.521
SSAS^[38]	0.264	2.320	25.027	2.625	3.462
Grad-PU^[52]	0.245	2.369	23.348	1.893	2.875
Ours	0.236	2.003	21.458	2.437	3.259

图6 在PU-GAN数据集上的定性比较

Fig. 6 Qualitative results on PU-GAN dataset ((a) Input; (b) PU-Net[3]; (c) MPU[10]; (d) PU-GAN[11]; (e) PU-GCN[12]; (f) Dis-PU[15]; (g) SSAS[38]; (h) Grad-PU[52]; (i) Ours; (j) GT)

表2 在PU1K数据集上的定量比较

Table 2 Quantitative results on PU1K dataset

网络	CD↓	HD↓	EMD↓	P2F(avg)↓	P2F(std)↓
PU-Net^[3]	1.155	15.170	91.487	4.834	6.799
MPU^[10]	0.935	13.327	77.401	3.551	5.970
PU-GAN^[11]	0.873	12.146	68.534	3.189	5.682
PU-GCN^[12]	0.585	7.577	55.570	2.499	4.004
Dis-PU^[15]	0.541	8.348	53.687	2.964	5.209
SSAS^[38]	0.613	7.451	68.970	2.474	6.088
Grad-PU^[52]	0.403	3.743	55.487	1.480	2.468
Ours	0.413	3.184	47.452	2.364	2.413

图7 在PU1K数据集上的定性比较

Fig. 7 Qualitative results on PU1K dataset ((a) Input; (b) PU-Net[3]; (c) MPU[10]; (d) PU-GAN[11]; (e) PU-GCN[12]; (f) Dis-PU[15]; (g) SSAS[38]; (h) Grad-PU[52]; (i) Ours; (j) GT)

图8 在真实扫描点云上的比较

Fig. 8 Results on real scan dataset ((a) Input; (b) PU-GAN[11]; (c) PU-GCN[12]; (d) Ours)

表3 消融实验的定量比较

Table 3 Quantitative results of ablation experiments

网络	CD↓	HD↓	EMD↓	P2F(avg)↓	P2F(std)↓
模型1	0.827	17.622	54.275	7.080	8.666
模型2	0.785	16.560	55.281	6.147	8.048
模型3	0.511	4.169	49.188	3.890	3.961
Ours	0.413	3.184	47.452	2.364	2.413

图9 消融实验的定性比较((a)输入；(b)模型1；(c)模型2；(d)模型3；(e)本文；(d) GT)

Fig. 9 Qualitative results of ablation experiments ((a) Input; (b) Model 1; (c) Model 2; (d) Model 3; (e) Ours; (d) GT)

表4 动态图邻域大小的影响

Table 4 Effect of K on Dynamic Graph

模型			K
模型	10	15	20	25	30
CD↓	0.589	0.532	0.413	0.489	0.603

图10 输入点云添加噪声的结果比较

Fig. 10 Results on noisy input point clouds ((a) Input; (b) PU-GAN[11]; (c) PU-GCN[12]; (d) Ours)

图11 不同输入点数的结果

Fig. 11 Results on point clouds with varying size ((a) 256; (b) 512; (c) 1024; (d) 2048)

表5 参数量和推理时间对比

Table 5 Comparison of Parameter Count and Inference Time

数据集	参数/kb ↓	时间/s ↓
PU-Net^[3]	814.3	0.566
Dis-PU^[15]	1047.0	1.604
Grad-PU^[52]	67.1	0.384
Ours	2802.0	0.987

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DGOA：基于动态图和偏移注意力的点云上采样

DGOA: point cloud upsampling based on dynamic graph and offset attention

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