Beyond geometry: The power of texture in interpretable 3D person ReID

Abstract

This paper presents FusionTexReIDNet, a robust framework for 3D person re-identification that uniquely leverages UVTexture to enhance both performance and explainability. Unlike existing 3D person ReID approaches that simply overlay textures on point clouds, our method exploits the full potential of UVTexture through its high resolution and normalized coordinate properties. The framework consists of two main streams: a UVTexture stream that processes appearance features and a 3D stream that handles geometric information. These streams are fused through an effective combination of KNN, attribute-based, and explainable re-ranking strategies. Our approach introduces explainability to 3D person ReID through the visualization of activation maps on UVTextures, providing insights into the model’s decision-making process by highlighting discriminative regions. By incorporating the Intersection-Alignment Score derived from activation maps and visible clothing masks, we further improve the ReID accuracy. Extensive experiments demonstrate that FusionTexReIDNet achieves state-of-the-art performance across various scenarios, with Rank-1 accuracies of 98.5% and 89.7% Rank-1 on benchmark datasets, while providing interpretable results through its explainable component.