Adaptive and Azimuth-Aware Fusion Network of Multimodal Local Features for 3D Object Detection

TL;DR

This paper introduces an adaptive, azimuth-aware fusion network that effectively combines image and LiDAR data to improve 3D object detection by generating richer local features and ensuring orientation consistency.

Contribution

It proposes a novel adaptive and azimuth-aware fusion network that explicitly adjusts feature intensities and aligns orientations across modalities for enhanced 3D detection.

Findings

Improved detection accuracy on KITTI dataset.

Effective integration of image and LiDAR features.

Validation of adaptive fusion advantages.

Abstract

This paper focuses on the construction of stronger local features and the effective fusion of image and LiDAR data. We adopt different modalities of LiDAR data to generate richer features and present an adaptive and azimuth-aware network to aggregate local features from image, bird's eye view maps and point cloud. Our network mainly consists of three subnetworks: ground plane estimation network, region proposal network and adaptive fusion network. The ground plane estimation network extracts features of point cloud and predicts the parameters of a plane which are used for generating abundant 3D anchors. The region proposal network generates features of image and bird's eye view maps to output region proposals. To integrate heterogeneous image and point cloud features, the adaptive fusion network explicitly adjusts the intensity of multiple local features and achieves the orientation consistency between image and LiDAR data by introduce an azimuth-aware fusion module. Experiments are conducted on KITTI dataset and the results validate the advantages of our aggregation of multimodal local features and the adaptive fusion network.