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This paper introduces the Task-Aware Deformable Prediction (TADP) method for single-stage 3D object detection, addressing the challenge of adapting extracted features for multiple tasks. By implementing a triple feature refinement aggregation module and a multi-scale feature aggregation block, TADP enhances feature extraction and fusion in a task-aware manner. Experimental results on the KITTI dataset show that TADP achieves a car mAP of 80.91%, outperforming many existing state-of-the-art methods in the field.
TADP achieves an impressive 80.91% mAP on the KITTI dataset, setting a new benchmark for single-stage 3D object detection.
Most single-stage 3D object detectors complete different tasks with the same extracted features. Nevertheless, it is impossible to project features into a common space that is adaptive for all the tasks. We present a novel task-aware deformable prediction (TADP) method for single-stage 3D object detection to solve this problem. Firstly, a triple feature refinement aggregation module is designed to extract three-level features adaptively. Additionally, we design the multi-scale feature aggregation block to fuse multi-scale features in a scale-aware manner. Finally, the prediction of each task is deformed with the designed plug-and-play task-aware deformation head. It can percept the emphasis and interaction of each task. We also designed three different deformation modules. The experimental results demonstrate that the proposed deformation head shows good results on other detection methods. The experimental results on the KITTI dataset demonstrate that the car mAP is 80.91%, surpassing many state-of-the-art methods on the KITTI benchmark.
