面向网页的BIM场景在线漫游研究

毛洋洋; 田瑾; 闫丰亭; 张玉金

doi:10.12299/jsues.24-0025

面向网页的BIM场景在线漫游研究

doi: 10.12299/jsues.24-0025

1.
上海工程技术大学电子电气工程学院, 上海 201620
2.
上海擎玺智能科技有限公司, 上海 201800

基金项目: 国家自然科学基金(U2033218)

详细信息

作者简介:
毛洋洋（1997 − ），男，硕士生，研究方向为计算机应用仿真。E-mail：18599141728@163.com

通讯作者:
田　瑾（1982 − ），女，副教授，博士，研究方向为计算机应用仿真。E-mail：jintian0120@foxmail.com

中图分类号: TP399
计量
- 文章访问数: 6
- HTML全文浏览量: 3
- PDF下载量: 2
- 被引次数: 0
出版历程
- 收稿日期: 2024-01-22
- 刊出日期: 2025-05-19

Research on web-based BIM scene online roaming

1.
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2.
Shanghai Qingxi Intelligent Technology Co., Ltd., Shanghai 201800, China

摘要

摘要: 针对目前有限的硬件性能和网络带宽无法满足三维场景海量数据的实时渲染问题，提出一套面向网页的建筑信息模型(BIM)场景漫游方案。首先，对BIM模型进行重构，并基于一种自顶而下的层次结构完成场景构件的细粒度导出；然后，基于顶点间的空间位置和构件间的相似计算完成模型压缩，缩短场景的显示时延；最后，基于视锥球和视锥体双层架构设计一种可视构件拾取算法，以离线的构件编码完成场景选择渲染，减轻硬件负载。选取5处不同规模场景进行验证，结果表明，平均压缩率可达45%，可在网页中满足GB规模场景的30 帧/s流畅漫游。
- 场景漫游 /
- 粒度处理 /
- 模型压缩 /
- 双层设计
Abstract: In response to the challenge that current hardware performance and network bandwidth are insufficient to support real-time rendering of massive data in 3D scenes, a web-based building information modeling (BIM) scene roaming solution was proposed. Initially, the BIM model was reconstructed, and a top-down hierarchical structure was employed to achieve fine-grained instances of scene components. Subsequently, model compression was completed based on the spatial position of vertices and the similarity calculation between components to shorten the display delay of the scene. Finally, a visual component picking algorithm based on the two-layer architecture of view frustum ball and view frustum was designed to complete scene selection and rendering by off-line component number and reduce hardware load. Five different scale scenarios were selected for validation. The results demonstrate an average compression rate of 45%, which enables smooth 30 frames per second roaming for GB-scale scene in web-based applications.
- scene roaming /
- granularity processing /
- model compression /
- two-layer design

HTML全文

图 1 层次结构示意图

Figure 1. Diagram of hierarchical structure

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图 2 重叠顶点示意图

Figure 2. Diagram of overlapping vertices

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图 3 视锥体示意图

Figure 3. Schematic diagram of the view frustum

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图 4 层次检测示意图

Figure 4. Diagram of hierarchical detection

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图 5 视锥球俯视图

Figure 5. Top view of frustum ball

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图 6 测试场景

Figure 6. Test scenes

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图 7 模型压缩

Figure 7. Model compression

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图 8 场景快照

Figure 8. Scene snapshot

下载: 全尺寸图片幻灯片

Input：模型文件.
Output：场景构件.
1：加载并解析模型文件；
2：自顶向下构建层次结构；
3：for 每个场景节点P_i do
4： if 节点P_i是Mesh类型 then
5：提取节点P_i的图元，添至队列Q；
6： else
7：判断节点P_i子节点类型；
8：end for
9：for 队首节点Q_j的图元信息 do
10：将Q_j的图元数据转成Blob数据；
11：将Blob数据赋给HTML超链接，执行下载属性；
12：对导出构件统一编码j.gltf；
13：end for

下载: 导出CSV

Input：场景模型.
Output：可视编码列表List.
1：构建层次结构，递归层次节点；
2：构建视锥球，开始双层检测；
3：for 每一个层次节点 do
4： if 内部节点与视锥球非外离 then
5： for 其下属的每一个叶节点 do
6： if 叶节点与视锥体内含/相交 then
7：获取构件编码，置入列表List；
8： else
9：当前节点及其下属节点皆不可见；
10： end for
11： else
12：当前节点及其下属节点皆不可见；
13：end for

下载: 导出CSV

表 1 不同压缩算法效果对比

Table 1. Effects comparison of different compression algorithms

场景	S_O/MB	IRMC		ACC4IFC		Our
场景	S_O/MB	S_C/MB	C_R/%	S_C/MB	C_R/%	S_C/MB	C_R/%
场景1	114	93.6	17.9	95.5	16.2	71.8	37
场景2	165	131.7	20.2	128.4	22.2	92.4	44
场景3	244	206.4	15.4	217.6	10.8	170.8	30
场景4	426	322.9	24.2	327.2	23.2	208.7	51
场景5	957	596.2	37.7	624.9	34.7	344.5	64

下载: 导出CSV

表 2 各场景在不同压缩算法后完整显示时间对比

Table 2. Complete display time comparison of each scene after different compression algorithms

场景	加载时间/s
场景	无压缩	IRMC	ACC4IFC	Our
场景1	6.0	5.2	5.3	3.9
场景2	8.8	6.9	8.1	5.7
场景3	14.4	12.9	13.6	6.5
场景4	18.1	14.7	14.3	7.8
场景5	35.5	24.8	26.1	11.8

下载: 导出CSV

表 3 各场景在不同可视检查算法中帧率对比

Table 3. Frame rate comparison of each scene in different visual inspection algorithms

场景	帧率/(帧·s⁻¹)
场景	BIMviews	文献[18]	文献[17]	本研究方法
场景1	40	38	48	44
场景2	29	9	44	42
场景3	24	0	36	37
场景4	0	0	30	36
场景5	0	0	21	32

下载: 导出CSV

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