TH-Net: A Method Of Single 3d Object Tracking Based On Transformers And Hausdorff Distance

3D object tracking is the key of automatic driving. We propose a new 3D object tracking method called Transformer-Hausdorff Net (TH-Net). It contains three main modules: Feature Extraction, Feature Fusion, and Proposal Generation. The Feature Extraction module extracts features from the template and search area, where the permutation-invariable Transformers is leveraged to deal with the point cloud’s dis-order and sparsity. The features of template and search area are then fused by the Feature Fusion module to generate the tracking clues. Based on the tracking clues, we further generate 3D target proposal and execute verification in Proposal Generation module. TH-Net achieves a performance improvement in contrast to the state-of-the-art work on KITTI and NuScenes dataset.