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MuRF-Net: Multi-Receptive Field Pillars for 3D Object Detection from Point Cloud
- Source :
- 2020 IEEE Intelligent Vehicles Symposium (IV).
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- In this paper, we propose a point cloud based 3D object detection framework that accounts for both contextual and local information by leveraging multi-receptive field pillars, named as MuRF-Net. Recently, common pipelines can be divided into a voxel-based feature encoder and an object detector. During the feature encoding steps, contextual information is neglected, which is critical for the 3D object detection task. Thus, the encoded features are not suitable to input to the subsequent object detector. To address this challenge, we propose the MuRF-Net with a multi-receptive field voxelization mechanism to capture both contextual and local information. After the voxelization, the voxelized points (pillars) are processed by a feature encoder, and a channel-wise feature reconfiguration module is proposed to combine the features with different receptive fields using a lateral enhanced fusion network. In addition, to handle the increase of memory and computational cost brought by multi-receptive field voxelization, a dynamic voxel encoder is applied taking advantage of the sparseness of the point cloud. Experiments on the KITTI benchmark for both 3D object and Bird's Eye View (BEV) detection tasks on car class are conducted and MuRF-Net achieved the state-of-the-art results compared with other voxel-based methods. Besides, the MuRF-Net can achieve nearly real-time speed with 20Hz.
- Subjects :
- business.industry
Computer science
010401 analytical chemistry
Feature extraction
Point cloud
010501 environmental sciences
Object (computer science)
01 natural sciences
Object detection
Field (computer science)
0104 chemical sciences
Feature (computer vision)
Encoding (memory)
Computer vision
Artificial intelligence
business
Encoder
0105 earth and related environmental sciences
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 IEEE Intelligent Vehicles Symposium (IV)
- Accession number :
- edsair.doi...........2458463f2d8a2b3baf1bdb5556b685f6