Instance by Instance: An Iterative Framework for Multi-instance 3D Registration
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916117142831104 |
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| author | Cao, Xinyue Zhang, Xiyu Cheng, Yuxin Qi, Zhaoshuai Zhang, Yanning Yang, Jiaqi |
| author_facet | Cao, Xinyue Zhang, Xiyu Cheng, Yuxin Qi, Zhaoshuai Zhang, Yanning Yang, Jiaqi |
| contents | Multi-instance registration is a challenging problem in computer vision and robotics, where multiple instances of an object need to be registered in a standard coordinate system. In this work, we propose the first iterative framework called instance-by-instance (IBI) for multi-instance 3D registration (MI-3DReg). It successively registers all instances in a given scenario, starting from the easiest and progressing to more challenging ones. Throughout the iterative process, outliers are eliminated continuously, leading to an increasing inlier rate for the remaining and more challenging instances. Under the IBI framework, we further propose a sparse-to-dense-correspondence-based multi-instance registration method (IBI-S2DC) to achieve robust MI-3DReg. Experiments on the synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance of IBI-S2DC, e.g., our MHF1 is 12.02%/12.35% higher than the existing state-of-the-art method ECC on the synthetic/real datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04195 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Instance by Instance: An Iterative Framework for Multi-instance 3D Registration Cao, Xinyue Zhang, Xiyu Cheng, Yuxin Qi, Zhaoshuai Zhang, Yanning Yang, Jiaqi Computer Vision and Pattern Recognition Multi-instance registration is a challenging problem in computer vision and robotics, where multiple instances of an object need to be registered in a standard coordinate system. In this work, we propose the first iterative framework called instance-by-instance (IBI) for multi-instance 3D registration (MI-3DReg). It successively registers all instances in a given scenario, starting from the easiest and progressing to more challenging ones. Throughout the iterative process, outliers are eliminated continuously, leading to an increasing inlier rate for the remaining and more challenging instances. Under the IBI framework, we further propose a sparse-to-dense-correspondence-based multi-instance registration method (IBI-S2DC) to achieve robust MI-3DReg. Experiments on the synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance of IBI-S2DC, e.g., our MHF1 is 12.02%/12.35% higher than the existing state-of-the-art method ECC on the synthetic/real datasets. |
| title | Instance by Instance: An Iterative Framework for Multi-instance 3D Registration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.04195 |