THOR2: Leveraging Topological Soft Clustering of Color Space for Human-Inspired Object Recognition in Unseen Environments

• URL: http://arxiv.org/abs/2408.01579v1
• Date: Fri, 2 Aug 2024 21:24:14 GMT
• Title: THOR2: Leveraging Topological Soft Clustering of Color Space for Human-Inspired Object Recognition in Unseen Environments
• Authors: Ekta U. Samani, Ashis G. Banerjee,
• Abstract summary: 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. THOR2, trained using synthetic data, demonstrates markedly improved recognition accuracy compared to THOR, its 3D shape-based predecessor.
• Score: 1.9950682531209158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 ViT adapted for RGB-D inputs on both the datasets. Therefore, THOR2 is a promising step toward achieving robust recognition in low-cost robots.

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