Three-dimensional object detection network based on geometric information supplement strategy

Abstract

Three-dimensional object detection from point clouds greatly advances autonomous driving. The real point clouds are unbalanced, where some occluded or distant objects with fewer points suffer from insufficient features, resulting in limited detection accuracy. To address this issue, we propose a novel three-dimensional object detection network based on a Geometric Information Supplement (GIS) strategy. Concretely, we design a novel shape completion model and a point attention fusion module to implement the GIS strategy. The shape completion model based on a structure-aware Transformer first recovers the complete shape points of objects within proposals. Then, the point attention fusion module supplies the recovered geometric shape cues to the original spatial features of objects. By the GIS strategy, the network learns enriched geometric features of objects for proposal refinement, thus, generating accurate detection boxes. Comparative experiments on KITTI dataset show that our method significantly outperforms other state-of-the-art methods while maintaining modest model complexity.