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| Main Authors: | , |
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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2408.01579 |
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| _version_ | 1866929629491625984 |
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| author | Samani, Ekta U. Banerjee, Ashis G. |
| author_facet | Samani, Ekta U. Banerjee, Ashis G. |
| contents | Visual object recognition in unseen and cluttered indoor environments is a challenging problem for mobile robots. This study presents a 3D shape and color-based descriptor, TOPS2, for point clouds generated from RGB-D images and an accompanying recognition framework, THOR2. The TOPS2 descriptor embodies object unity, a human cognition mechanism, by retaining the slicing-based topological representation of 3D shape from the TOPS descriptor while capturing object color information through slicing-based color embeddings computed using a network of coarse color regions. These color regions, analogous to the MacAdam ellipses identified in human color perception, are obtained using the Mapper algorithm, a topological soft-clustering technique. THOR2, trained using synthetic data, demonstrates markedly improved recognition accuracy compared to THOR, its 3D shape-based predecessor, on two benchmark real-world datasets: the OCID dataset capturing cluttered scenes from different viewpoints and the UW-IS Occluded dataset reflecting different environmental conditions and degrees of object occlusion recorded using commodity hardware. THOR2 also outperforms baseline deep learning networks, and a widely-used Vision Transformer (ViT) adapted for RGB-D inputs trained using synthetic and limited real-world data on both the datasets. Therefore, THOR2 is a promising step toward achieving robust recognition in low-cost robots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01579 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | THOR2: Topological Analysis for 3D Shape and Color-Based Human-Inspired Object Recognition in Unseen Environments Samani, Ekta U. Banerjee, Ashis G. Computer Vision and Pattern Recognition Visual object recognition in unseen and cluttered indoor environments is a challenging problem for mobile robots. This study presents a 3D shape and color-based descriptor, TOPS2, for point clouds generated from RGB-D images and an accompanying recognition framework, THOR2. The TOPS2 descriptor embodies object unity, a human cognition mechanism, by retaining the slicing-based topological representation of 3D shape from the TOPS descriptor while capturing object color information through slicing-based color embeddings computed using a network of coarse color regions. These color regions, analogous to the MacAdam ellipses identified in human color perception, are obtained using the Mapper algorithm, a topological soft-clustering technique. THOR2, trained using synthetic data, demonstrates markedly improved recognition accuracy compared to THOR, its 3D shape-based predecessor, on two benchmark real-world datasets: the OCID dataset capturing cluttered scenes from different viewpoints and the UW-IS Occluded dataset reflecting different environmental conditions and degrees of object occlusion recorded using commodity hardware. THOR2 also outperforms baseline deep learning networks, and a widely-used Vision Transformer (ViT) adapted for RGB-D inputs trained using synthetic and limited real-world data on both the datasets. Therefore, THOR2 is a promising step toward achieving robust recognition in low-cost robots. |
| title | THOR2: Topological Analysis for 3D Shape and Color-Based Human-Inspired Object Recognition in Unseen Environments |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.01579 |