DTSSD: Dual-Channel Transformer-Based Network for Point-Based 3D Object Detection
Zhijie Zheng, Zhicong Huang, Jingwen Zhao, Haifeng Hu, Dihu Chen
Abstract
In the field of 3D object detection, previous methods mainly utilize one channel feature encoding network to extract point-wise features. Despite the effectiveness, we find that only leveraging one channel encoding network is not sufficient and impedes the detection performance. To this end, we propose a dual-channel transformer-based feature encoding network, which integrates both set abstraction layer and transformer block as backbone. It enables the model to exploit fine-grained as well as long-range contextual information of objects, thus providing complementary relationship of two methods. In addition, a centroid estimation module is introduced to obtain powerful representation of the whole object. Finally, considering the significance of point density, which is crucial for detection performance, we propose a central density-aware enhancement module to equip center features with distinct density features. Experimental results on KITTI dataset show the effectiveness of our proposed method.