3D Reconstruction of Unstructured Objects Using Information From Multiple Sensors

点云 由运动产生的结构 计算机科学 人工智能 计算机视觉 曲面重建 三维重建 分割 迭代重建 噪音(视频) 特征(语言学) 算法 重建算法 比例因子(宇宙学) 点(几何) 运动估计 曲面(拓扑) 数学 图像(数学) 几何学 语言学 哲学 物理 宇宙学 量子力学 空间的度量展开 暗能量
作者
Hui Chen,Fangyong Xu,Wanquan Liu,Dongge Sun,Peter Liu,Muhammad Ilyas Menhas,Bilal Ahmad
出处
期刊:IEEE Sensors Journal [IEEE Sensors Council]
卷期号:21 (23): 26951-26963 被引量:8
标识
DOI:10.1109/jsen.2021.3121343
摘要

The Structure-from-Motion (SfM) algorithm is widely used for point cloud reconstruction. However, one drawback of conventional SfM based methods is that the obtained final point sets may contain holes and noise, which could degrade the estimation of reconstructed objects especially for smooth surfaces with few features. The other drawback is the accuracy and speed of SfM based methods depend on the uncertain number of images. To overcome these limitations, this paper proposes a novel 3D reconstruction method for unstructured objects based on the structure from motion in combination with the structured light, in which the point sets of structured light and the point sets of structure from motion can come from different target objects. Since the two point sets coming from multiple sensors do not scale well for register, making it difficult to find corresponding points, a scaled principal component analysis algorithm is proposed for the registration to overcome the impact due to large scale variance. With a large scale factor, a recalculated registration center is proposed via feature region segmentation to achieve point cloud registration again. The two point sets are matched using the proposed optimization method to complete 3D reconstruction. Surface reconstruction is performed using the Poisson algorithm to obtain a smooth surface. The proposed method is tested on some simple structured objects and real-life data of complex unstructured objects collected using range sensors. Compared with several state-of-the-art algorithms, experimental results confirm its potential for surface reconstruction from depth data calculated from the two sets.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
九号发布了新的文献求助10
刚刚
刚刚
Liugz完成签到,获得积分10
2秒前
2秒前
3秒前
英俊的铭应助kkk采纳,获得10
3秒前
干羞花完成签到,获得积分10
4秒前
爆米花应助静越采纳,获得10
5秒前
LSD发布了新的文献求助30
7秒前
赘婿应助小林不熬夜采纳,获得10
7秒前
深情安青应助飞飞采纳,获得10
7秒前
Bryan应助杨Eason采纳,获得10
7秒前
chaobada发布了新的文献求助10
7秒前
yull发布了新的文献求助10
8秒前
MOMO完成签到,获得积分10
8秒前
9秒前
ningzi完成签到,获得积分10
10秒前
莫寻双完成签到,获得积分10
10秒前
Rondab应助科研通管家采纳,获得10
12秒前
wy.he应助科研通管家采纳,获得10
12秒前
今后应助科研通管家采纳,获得10
12秒前
12秒前
Rondab应助科研通管家采纳,获得10
12秒前
所所应助科研通管家采纳,获得10
12秒前
酷波er应助科研通管家采纳,获得10
12秒前
12秒前
12秒前
12秒前
12秒前
12秒前
13秒前
追寻冰淇淋应助文静采纳,获得10
13秒前
123发布了新的文献求助10
13秒前
14秒前
LSD关闭了LSD文献求助
15秒前
冲浪男孩226完成签到 ,获得积分10
16秒前
16秒前
东郭千愁完成签到,获得积分10
18秒前
小诗发布了新的文献求助10
19秒前
ZHANG完成签到,获得积分10
19秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969513
求助须知:如何正确求助?哪些是违规求助? 3514327
关于积分的说明 11173617
捐赠科研通 3249672
什么是DOI,文献DOI怎么找? 1794973
邀请新用户注册赠送积分活动 875537
科研通“疑难数据库(出版商)”最低求助积分说明 804836