改进的3D-BoNet算法应用于点云实例分割与三维重建

doi:10.13474/j.cnki.11-2246.2024.0606

测绘通报 ›› 2024, Vol. 0 ›› Issue (6): 30-35.doi: 10.13474/j.cnki.11-2246.2024.0606

• 学术研究 • 上一篇

改进的3D-BoNet算法应用于点云实例分割与三维重建

郭宝云¹, 姚玉凯¹, 李彩林^1,2, 王悦¹, 孙娜¹, 鲁一慧³

1. 山东理工大学建筑工程与空间信息学院, 山东淄博 255000;
2. 武汉大学湖北珞珈实验室, 湖北武汉 430070;
3. 山东省国土测绘院, 山东济南 250013

收稿日期:2023-11-27 发布日期:2024-06-27
通讯作者: 鲁一慧。E-mail:luyihui@shandong.cn
作者简介:郭宝云(1986—),女,博士,副教授,主要研究方向为低空摄影测量与遥感。E-mail:691473412@qq.com
基金资助:
山东省自然科学基金(ZR2022MD039);湖北珞珈实验室开放基金(230100026)

Application of improved 3D-BoNet to segmentation and 3D reconstruction of point cloud instances

GUO Baoyun¹, YAO Yukai¹, LI Cailin^1,2, WANG Yue¹, SUN Na¹, LU Yihui³

1. School of Architectural Engineering and Spatial Information, Shandong University of Technology, Zibo 255000, China;
2. Hubei Luojia Laboratory, Wuhan University, Wuhan 430070, China;
3. Shandong Provincial Institute of Land Surveying and mapping, Jinan 250013, China

Received:2023-11-27 Published:2024-06-27

摘要/Abstract

摘要： 为了更好地利用点云数据重建室内三维模型,本文提出了一种基于3D-BoNet-IAM算法的室内场景三维重建方法。该方法通过改进3D-BoNet算法提高点云数据的实例分割精度。针对点云数据缺失问题,提出了基于平面基元合并优化的拟合平面方法,利用拟合得到的新平面重建建筑表面模型。在S3DIS和ScanNet V2数据集上验证3D-BoNet算法的改进效果。试验结果表明,本文提出的3D-BoNet-IAM算法比原始算法分割精度提高了3.3%;对比本文建模效果与其他建模效果发现,本文方法的建模效果更准确。本文方法能够提高室内点云数据的实例分割精度,同时得到高质量的室内三维模型。

关键词: 点云数据, 3D-BoNet-IAM, 三维重建, 实例分割, 平面基元

Abstract: In order to better utilize point cloud data to reconstruct indoor 3D models, this paper proposes a 3D reconstruction method for indoor scenes based on 3D-BoNet-IAM algorithm. The method improves the instance segmentation accuracy of the point cloud data by improving the 3D-BoNet algorithm.For the problem of missing point cloud data, a method based on plane primitive merging optimization is proposed to fit the plane, and the new plane obtained from the fitting is used to reconstruct the building surface model. The improved effect of 3D-BoNet algorithm is verified on S3DIS and ScanNet V2 dataset, and it is proved through experiments that the algorithm of 3D-BoNet-IAM proposed in this paper improves the segmentation accuracy by 3.3% compared with the original algorithm; the modeling effect of this paper is compared with other modeling effects, and it is proved through comparisons that this paper’s modeling effect is more accurate. The method in this paper can improve the instance segmentation accuracy of indoor point cloud data, and at the same time obtain high-quality indoor 3D models.

Key words: point cloud data, 3D-BoNet-IAM, 3D reconstruction, instance segmentation, plane primitive

中图分类号:

P237

郭宝云, 姚玉凯, 李彩林, 王悦, 孙娜, 鲁一慧. 改进的3D-BoNet算法应用于点云实例分割与三维重建[J]. 测绘通报, 2024, 0(6): 30-35.

GUO Baoyun, YAO Yukai, LI Cailin, WANG Yue, SUN Na, LU Yihui. Application of improved 3D-BoNet to segmentation and 3D reconstruction of point cloud instances[J]. Bulletin of Surveying and Mapping, 2024, 0(6): 30-35.

参考文献

[1] 赖国棣.基于倾斜摄影和激光点云的城市实景三维模型构建研究[J].科学技术创新, 2023(27):25-28.
[2] 晋帅,李煊鹏,杨凤,等.伪激光点云增强的道路场景三维目标检测[J].中国图象图形学报,2023,28(11): 3520-3535.
[3] 吕猛.基于BIM技术的工程管理优化路径探讨[J].科技创新与应用,2023,13(32): 189-192.
[4] 李安琪,郑艳,王博,等.基于3D点云深度学习上采样的建筑物精细化三维重建[J].现代信息科技,2023,7(21): 136-139.
[5] BIBER P,ANDREASSON H,DUCKETT T,et al.3D modeling of indoor environments by a mobile robot with a laser scanner and panoramic camera[C]//Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Sendai:IEEE,2004: 3430-3435.
[6] YANG Bo,WANG Jianan,CLARK R,et al.Learning object bounding boxes for 3D instance segmentation on point clouds[EB/OL].2019-10-08[2023-10-02].http://arxiv.org/abs/1906.01140.
[7] Derpanis K G.Overview of the RANSAC algorithm[J].Image Rochester NY,2010,4(1): 2-3.
[8] GUO Haoyu,PENG Sida,LIN Haotong,et al.Neural 3D scene reconstruction with the manhattan-world assumption[C]//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).New Orleans:IEEE,2022: 5501-5510.
[9] GAMAL A,WIBISONO A,WICAKSONO S B,et al.Automatic LiDAR building segmentation based on DGCNN and euclidean clustering[J].Journal of Big Data,2020,7(1): 102.
[10] HUANG Jin,STOTER J,PETERS R,et al.City3D: large-scale building reconstruction from airborne LiDAR point clouds[J].Remote Sensing,2022,14(9): 2254.
[11] WANG Weiyue,YU R,HUANG Qiangui,et al.SGPN: similarity group proposal network for 3D point cloud instance segmentation[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:2569-2578.
[12] YI Li,ZHAO Wang,WANG He,et al.GSPN: generative shape proposal network for 3D instance segmentation in point cloud[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Long Beach:IEEE,2019: 3942-3951.
[13] HOU Ji,DAI A,NIEßNER M.3D-SIS: 3D semantic instance segmentation of RGB-D scans[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Long Beach:IEEE,2019: 4416-4425.
[14] IKEHATA S,YANG Hang,FURUKAWA Y.Structured indoor modeling[C]//Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV).Santiago:IEEE,2015: 1323-1331.
[15] MURA C,MATTAUSCH O,PAJAROLA R.Piecewise-planar reconstruction of multi-room interiors with arbitrary wall arrangements[J].Computer Graphics Forum,2016,35(7): 179-188.
[16] NAN Liangliang,WONKA P.PolyFit: polygonal surface reconstruction from point clouds[C]//Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV).Venice:IEEE,2017: 2372-2380.

改进的3D-BoNet算法应用于点云实例分割与三维重建

Application of improved 3D-BoNet to segmentation and 3D reconstruction of point cloud instances

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