Scale-Aware Attention-Based PillarsNet (SAPN)‎ Based 3D Object Detection for Point Cloud

Abstract EN

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.