Three-dimensional object detection can provide precise positions of objects, which can be beneficial to many robotics applications, such as self-driving cars, housekeeping robots, and autonomous navigation. In this work, we focus on accurate object detection in 3D point clouds and propose a new detection pipeline called scale-aware attention-based PillarsNet (SAPN). SAPN is a one-stage 3D object detection approach similar to PointPillar. However, SAPN achieves better performance than PointPillar by introducing the following strategies. First, we extract multiresolution pillar-level features from the point clouds to make the detection approach more scale-aware. Second, a spatial-attention mechanism is used to highlight the object activations in the feature maps, which can improve detection performance. Finally, SE-attention is employed to reweight the features fed into the detection head, which performs 3D object detection in a multitask learning manner. Experiments on the KITTI benchmark show that SAPN achieved similar or better performance compared with several state-of-the-art LiDAR-based 3D detection methods. The ablation study reveals the effectiveness of each proposed strategy. Furthermore, strategies used in this work can be embedded easily into other LiDAR-based 3D detection approaches, which improve their detection performance with slight modifications.
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