THOR2: Topological Analysis for 3D Shape and Color-Based Human-Inspired Object Recognition in Unseen Environments

TL;DR

THOR2 introduces a topological 3D shape and color descriptor for improved object recognition in cluttered, unseen indoor environments, outperforming existing methods and deep learning models.

Contribution

It proposes TOPS2, a novel descriptor combining topological shape and color features, and a recognition framework that enhances accuracy in challenging real-world scenarios.

Findings

THOR2 outperforms THOR and baseline deep learning models on benchmark datasets.

The descriptor effectively captures object shape and color for recognition.

Recognition accuracy significantly improves in cluttered and occluded scenes.

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 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.