Multi-Scale Keypoints Feature Fusion Network for 3D Object Detection from Point Clouds

초록

Lidar based object detection is utilized in numerous applications. The point-based and voxel-based methods employ furthest point sampling (FPS) algorithms and feature up-sampling to reduce the high computation cost respectively. In addition, aggregating all feature vectors for prediction leads to a large cost, the object confidence and location estimate are affected. Therefore, we propose a novel multi-scale keypoints feature fusion framework for 3D object detection, which take advantage of the 3D voxel convolutional neural network and PointNet-based set abstraction to learn more discriminative point features. A feature FPS set abstraction module is proposed to aggregate 3D voxel-wise features which can handle point loss and redundancy with learning complex features. A multi-scale feature fusion strategy is used to acquire context information and location information with multiple receptive fields, which reduces the computation cost. Finally, a refine prediction head is designed to improve box refinement and confidence prediction. We evaluate our model on benchmark KITTI which exhibit good performance and achieved 2.09% improvement in car hard difficulty. The feature FPS set abstraction module and fusion strategy outperform the state-of-the-arts work by 2.13% in all categories at different difficulties.

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