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arXiv · 1907.05286

Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds

Bei Wang·Jianping An·Jiayan Cao
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Abstract

Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications. In this work, we present Voxel-FPN, a novel one-stage 3D object detector that utilizes raw data from LIDAR sensors only. The core framework consists of an encoder network and a corresponding decoder followed by a region proposal network. Encoder extracts multi-scale voxel information in a bottom-up manner while decoder fuses multiple feature maps from various scales in a top-down way. Extensive experiments show that the proposed method has better performance on extracting features from point data and demonstrates its superiority over some baselines on the challenging KITTI-3D benchmark, obtaining good performance on both speed and accuracy in real-world scenarios.

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Bei Wang, Jianping An, Jiayan Cao. 2019-06-28. Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds. https://arxiv.org/abs/1907.05286

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