RMTrack: 6D Object Pose Tracking by Continuous Image Render Match | |
Cao Enyuan1,2; Zhu Xiaoyang1; Yu Haitao1; Jiang Yongshi1 | |
2022 | |
会议名称 | the 5th International Conference on Image and Graphics Processing (ICIGP) |
会议日期 | 2022年1月7-9日 |
会议地点 | 网络 |
摘要 | Estimating the 6D pose of a known object has very important applications in augmented reality and robot operations. This problem is challenging because of the clutter of the scene, the diversity of objects, and the complexity of lighting and textures. In this work, we propose a deep learning architecture for 6D object pose estimation, and a neural network that can predict object movement. By learning to predict the relative pose between the observation of current frame and the rendered image of previous prediction, the pose of the object can be tracked robustly for a long time. We have also introduced an efficient way of representing object motion, which can reduce the influence of the field of view and object scale so that this method has a strong cross-dataset generalization. We have conducted a lot of experiments on the LINEMOD dataset, the OccludedLINEMOD dataset, and the YCB dataset to show that this method can provide accurate pose estimation using only color images as input while being highly robust to occlusion. |
收录类别 | EI |
七大方向——子方向分类 | 计算机图形学与虚拟现实 |
国重实验室规划方向分类 | 其他 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49940 |
专题 | 综合信息系统研究中心_视知觉融合及其应用 |
通讯作者 | Cao Enyuan |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Cao Enyuan,Zhu Xiaoyang,Yu Haitao,et al. RMTrack: 6D Object Pose Tracking by Continuous Image Render Match[C],2022. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
CP0012.pdf(519KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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