Camera-and LiDAR-based Person Re-Identification

Abstract

In this paper, we introduce a novel method for creating appearance embeddings to identify individual persons using an object re-identification (ReID) framework. We present CLFormer (Camera LiDAR Transformer), a transformer-based architecture that incorporates multi-modal data from both camera and LiDAR sensors. We introduce the 3D Cuboid-Inclusive Point Embedding (3D-CIPE), which leverages rich data from LiDAR point clouds and 3D cuboids to add a learnable embedding into the transformer structure. Additionally, through ablation studies, we explore and analyze various strategies for the early and late fusion of multi-modal input data. To evaluate our proposed CLFormer, we reinterpret the nuScenes dataset [1] for ReID purposes and use it for our experiments. Our method demonstrates a significant improvement in performance, outperforming the image-only baseline with an increase of 2.3 in mean Average Precision (mAP).