Point cloud 3D object detection method based on density information-local feature fusion

Nowadays, three-dimensional (3D) point cloud is widely used in unmanned driving, high-precision mapping, robot grasping, mapping and virtual reality (VR) / augmented reality (AR), etc. Especially, many studies have focused on object detection through directly processing point cloud, but they don’t take into account the uneven density of point clouds on the surface of object and lack of feature information. Inspired by this, we propose a new 3D object detection method based on density information-local feature fusion for point cloud. Firstly, the 3D coordinate features, high-dimensional features and density features of the point cloud are extracted through the backbone feature extraction network, and the local features of the point cloud are extracted through the sampling and grouping operation. Secondly, attention mechanism is used to encode the information between local features with density information. Then, the voting network is used to make the point clouds return to the center of the object. Finally, the point clouds are clustered and proposed to generate 3D bounding boxes. The proposed method can reduce the influence brought by the uneven sampling of point cloud and enhance the feature information of object, thereby improving the accuracy of 3D object detection. Specifically, the proposed method is validated on the SUNRGB-D and ScanNet datasets. Through various experiments, we confirm the proposed method’s effectiveness and robustness to improve the performance of 3D object detection.