Spatial-Aware Learning in Feature Embedding and Classification for One-Stage 3-D Object Detection

One-stage 3D object detection, known for its simplicity and high-speed inference, is attracting increasing attention in autonomous driving scenarios. However, current one-stage detectors tend to perform sub-optimally compared to two-stage competitors. Our experimental findings suggest that one-stage detectors underperform due to the underutilization of spatial information in feature embedding and classification. Concretely, the spatial context is severely lost during feature propagation, inducing distorted spatial awareness. On the other hand, category recognition relies on the full utilization of spatial information, which is neglected by current detectors. This inadequate spatial awareness of the classification branch can exacerbate misclassification. To address these issues, we propose Spatial-aware Learning in Feature Embedding and Classification for One-stage 3D Object Detection (SLDet). Specifically, to restore the distorted spatial awareness, Category-wise Spatial Augmentation (CSA) is proposed to adaptively bring the network with pre-encoding multi-scale spatial contexts. As for misclassification, Spatial Guiding Classification (SGC) is introduced to guide the classification using explicit scale information. It employs the natural scale divergences among categories to rectify misclassification. Comprehensive experiments demonstrate that SLDet efficiently utilizes spatial information and achieves newly state-of-the-art performance on both the Waymo Open Dataset and the ONCE Dataset. Furthermore, additional experiments demonstrate the excellent generalization capacity of SLDet.