Graph Neural Network based on Geometric and Appearance Attention for 6D Pose Estimation
文献类型：会议论文
| 作者 | Wang TF(王天夫)1,2,3; Wang HG(王洪光)1,2
|
| 出版日期 | 2021 |
| 会议日期 | September 17-19, 2021 |
| 会议地点 | Virtual, Online, China |
| 关键词 | 6D pose estimation Graph neural network Colored point clouds |
| 页码 | 122-127 |
| 英文摘要 | Object 6D pose estimation from RGB-D images requires extracting useful information from two complementary data sources (color and depth). Usually, previous methods extract from two networks (for color and depth) separately and concatenate the final features. In this work, we propose a novel attention based graph neural network to process color and depth images concurrently. In this way, we can learn the interactions between depth and color, leading to an effective fusion. Specifically, our method consists of two stages. First, we convert the segmentation of an RGB-D image into colored point clouds. Second, we estimate the poses of objects by an attention based graph neural network. Our attention mechanism is based on geometric and appearance information. In this way, 3D geometric and 2D appearance information can be fully utilized for better feature learning. The experimental results on LineMOD and Occlusion LineMOD datasets show the effectiveness of our method. |
| 产权排序 | 1 |
| 会议录 | AIPR 2021 - 2021 4th International Conference on Artificial Intelligence and Pattern Recognition
 |
| 会议录出版者 | ACM |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-4503-8408-7 |
| 源URL | [http://ir.sia.cn/handle/173321/30577]  |
| 专题 | 工艺装备与智能机器人研究室 |
| 通讯作者 | Wang TF(王天夫) |
| 作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.University of Chinese Academy of Sciences, Beijing 100049, China |
| 推荐引用方式 GB/T 7714 | Wang TF,Wang HG. Graph Neural Network based on Geometric and Appearance Attention for 6D Pose Estimation[C]. 见:. Virtual, Online, China. September 17-19, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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