Yuqing Qian

Three-dimensional object detection plays a crucial role in achieving accurate and reliable autonomous driving systems. However, the current state-of-the-art two-stage detectors lack flexibility and have limited feature extraction capabilities to effectively handle the disorder and irregularity of point clouds. In this paper, we propose a novel network called FANet, which combines the strengths of PV-RCNN and PAConv (position adaptive convolution). The goal of FANet is to address the irregularity and disorder present in point clouds. In our network, the convolution operation constructs convolutional kernels using a basic weight matrix, and the coefficients of these kernels are adaptively learned by LearnNet from relative points. This approach allows for the flexible modeling of complex spatial variations and geometric structures in 3D point clouds, leading to the improved extraction of point cloud features and generation of high-quality 3D proposal boxes. Compared to other methods, extensive experiments on the KITTI dataset have demonstrated that the FANet exhibits superior 3D object detection accuracy, showcasing a significant improvement in our approach.

3D object detection is essential for an accurate and reliable autonomous driving system. Currently, the methods used by the state-of-the-art two-stage detectors are not flexible enough and their fea-ture extraction capabilities are very limited to cope effectively with the disorder and irregularity of point clouds. In this paper, we combine the advantages of both PV-RCNN and PAConv (Position Adaptive Convolution) to create a completely new network, FANet, in order to overcome the ir-regularity and disorder of point clouds. The convolution in our network builds convolutional ker-nels from a basic weight matrix, whose combined coefficients are learned adaptively by LearnNet from relative points. This network allows for flexible modeling of complex spatial variations and geometric structures in the 3D point cloud, enabling better extraction of point cloud features and producing high-quality 3D proposal boxes. Compared to other methods, FANet is superior in terms of 3D object detection accuracy. Extensive experiments on the KITTI dataset have shown a signif-icant improvement in our approach.