THOR2: Leveraging Topological Soft Clustering of Color Space for Human-Inspired Object Recognition in Unseen Environments
<|reference_start|>THOR2: Leveraging Topological Soft Clustering of Color Space for Human-Inspired Object Recognition in Unseen Environments: 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.<|reference_end|>
@article{samani2024thor2:,
    title={THOR2: Leveraging Topological Soft Clustering of Color Space for
  Human-Inspired Object Recognition in Unseen Environments},
    author={Ekta U. Samani and Ashis G. Banerjee},
    journal={arXiv preprint arXiv:2408.01579},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01579},
    primaryClass={cs.CV}
}