IAN: Instance-Augmented Net for 3D Instance Segmentation
文献类型：期刊论文
| 作者 | Wan, Zihao1,2; Hu, Jianhua2 ; Zhang, Haojian2; Wang, Yunkuan2
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| 刊名 | IEEE ROBOTICS AND AUTOMATION LETTERS
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| 出版日期 | 2023-07-01 |
| 卷号 | 8期号:7页码:4354-4361 |
| ISSN号 | 2377-3766 |
| 关键词 | Three-dimensional displays Feature extraction Point cloud compression Solid modeling Semantics Noise measurement Aggregates Deep learning for visual perception RGB-D perception data sets for robotic vision |
| DOI | 10.1109/LRA.2023.3281905 |
| 通讯作者 | Wang, Yunkuan(yunkuan.wang@ia.ac.cn) |
| 英文摘要 | When aggregating local information from neighbors, prevailing 3D instance segmentation backbones only leverage 3D coordinates to find neighboring points without identifying whether these points are from the same object as the query point, which causes the model to gather excessive noisy features. Besides, traditional backbones fail to fully utilize multi-resolution information. Therefore, previous methods have difficulty in segmenting targets in cluttered scenes. To tackle these issues, we propose Instance-Augmented Net (IAN). The keys to our approach are Instance-Augmented Block (IAB), Instance-Augmented Upsampler (IAU), and Attentive Fusion (AF). In IAB, for each foreground point, we leverage its instance information to filter out noisy neighbors from other objects. We also propose IAU to apply this instance-augmented strategy to the upsampling process. Furthermore, to retain comprehensive information, we upsample multi-resolution feature maps and adopt attention generated by AF to fuse them. Notably, by encoding neighborhood information, AF can generate attention at point-level adaptively. Moreover, to further test the generality of models, we present Clutter and Occlusion (CAO), a new 3D instance segmentation dataset tailored for robotic grasping tasks. Extensive experiments on S3DIS, ScanNet and CAO show the effectiveness of our IAN. |
| WOS研究方向 | Robotics |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001012840800007 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53534]  |
| 专题 | 智能制造技术与系统研究中心_先进制造与自动化 |
| 通讯作者 | Wang, Yunkuan |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wan, Zihao,Hu, Jianhua,Zhang, Haojian,et al. IAN: Instance-Augmented Net for 3D Instance Segmentation[J]. IEEE ROBOTICS AND AUTOMATION LETTERS,2023,8(7):4354-4361. |
| APA | Wan, Zihao,Hu, Jianhua,Zhang, Haojian,&Wang, Yunkuan.(2023).IAN: Instance-Augmented Net for 3D Instance Segmentation.IEEE ROBOTICS AND AUTOMATION LETTERS,8(7),4354-4361. |
| MLA | Wan, Zihao,et al."IAN: Instance-Augmented Net for 3D Instance Segmentation".IEEE ROBOTICS AND AUTOMATION LETTERS 8.7(2023):4354-4361. |
入库方式： OAI收割
来源：自动化研究所
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