A more repeatable and robust local reference frame for 3D local surface description
文献类型：会议论文
| 作者 | Lu RR(鲁荣荣)1,2,3,4 ; Zhu F(朱枫)1,2,3; Wu QX(吴清潇)1,2,3; Fu XY(付兴银)1,2,3,4
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| 出版日期 | 2018 |
| 会议日期 | December 12-14, 2018 |
| 会议地点 | Chengdu, China |
| 关键词 | 3D local surface feature local reference frame point cloud |
| 页码 | 1-9 |
| 英文摘要 | Building 3D local surface feature with a local reference frame (LRF) can obtain rotational invariance and make use of 3D spatial information, thereby boosting the distinctiveness of a 3D local feature. However, this situation is based on the assumption that the LRF is stable and repeatable. Owing to the disturbances like noise, point density variation, occlusion and clutter, LRF may suffer ambiguity so that limit the ability of a LRF-based 3D local feature. This paper presents an efficient method for LRF construction. The experimental results show the superior performance of our proposed LRF in terms of repeatability and robustness on several popular datasets by comparing with the state-of-the-art methods. Moreover, our method is computational efficient as well. |
| 源文献作者 | China Graphics Society ; China Society of Image and Graphics ; Ocean University of China ; University of Portsmouth |
| 产权排序 | 1 |
| 会议录 | 2018 10th International Conference on Graphics and Image Processing (ICGIP 2018)
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| 会议录出版者 | SPIE |
| 会议录出版地 | Bellingham, USA |
| 语种 | 英语 |
| ISSN号 | 0277-786X |
| ISBN号 | 978-1-5106-2828-1 |
| WOS记录号 | WOS:000485096200037 |
| 源URL | [http://ir.sia.cn/handle/173321/24687]  |
| 专题 | 沈阳自动化研究所_光电信息技术研究室 |
| 通讯作者 | Zhu F(朱枫) |
| 作者单位 | 1.Shenyang Institute of Automation, CAS, Shenyang 110016, China 2.Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016, China 3.Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016, China 4.University of Chinese Academy of Sciences, Beijing 100049, China |
| 推荐引用方式 GB/T 7714 | Lu RR,Zhu F,Wu QX,et al. A more repeatable and robust local reference frame for 3D local surface description[C]. 见:. Chengdu, China. December 12-14, 2018. |
入库方式： OAI收割
来源：沈阳自动化研究所
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