A region feature fusion network for point cloud and image to detect 3D object

Author:

Shi Yanjun12ORCID,Ma Longfei1ORCID,Li Jiajian1,Wang Xiaocong1,Yang Yu1234

Affiliation:

1. School of Mechanical Engineering Dalian University of Technology Dalian China

2. Collective Intelligence & Collaboration Laboratory Beijing China

3. China North Artificial Intelligence & Innovation Research Institute Beijing China

4. China North Vehicle Research Institute NORINCO Unmanned Vehicle Research & Development Center Beijing China

Abstract

AbstractSensor fusion is very important for collaborative intelligent systems. A regional feature fusion network called ReFuNet for detecting 3D Object is proposed. It is difficult to detect distant or small objects accurately for the sparsity of LiDAR point cloud. The LiDAR point cloud and camera image information to solve the problem of point cloud sparsity is used, which can integrate image‐rich semantic information to enhance point cloud features. Also, the authors’ ReFuNet method segments the possible areas of objects by the results of 2D image detection. A cross‐attention mechanism adaptively fuses image and point cloud features within the areas. Then, the authors’ ReFuNet uses fused features to predict the 3D bounding boxes of objects. Experiments on the KITTI 3D object detection dataset showed that the authors’ proposed fusion method effectively improved the performance of 3D object detection.

Publisher

Institution of Engineering and Technology (IET)

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