# FANet: Improving 3D Object Detection with Position Adaptation
## 3 Methods  
Currently, the convolution used by 3D object detection in feature extraction is static [30]. In practical applications, this method often reduces the model accuracy and expressiveness ability due to insufficient computational resources [31]. This paper combines dynamic convolution to process irregular and disordered point cloud data through higher flexibility, which enables higher accuracy of 3D object detectors.  
Considering the existence of the above problems, this paper proposes a high-accuracy 3D object detector that can solve the above problems through dynamic convolution. In the 3D space of the point cloud, the relationship between points is complex. PAC can adapt the selection of the convolution kernel to the changes around it, and improve the accuracy of the 3D object detector. The overall framework of FANet is shown in Figure 1. This network incorporates PAConv into the Voxel Set Abstraction module, which aggregates features from 3D sparse convolutional neural networks and keypoints collected through furthest point sampling, and encodes them together. This, combined with the high-quality proposals generated through voxel generation, results in the generation of highly accurate 3D proposal boxes.  
The voxel set abstraction module of PV-RCNN is capable of learning features directly from point cloud keypoints and voxels generated by the 3D sparse neural network, exhibiting a multi-level feature extraction structure. In this paper, we embed PAConv within  
Figure 1: The overall framework of the FANet.  
the voxel set abstraction module to generate a dynamic network, as shown in Figure 2. In the dynamic network, dynamic network encoding is performed on each group of local neighborhoods, where the sampled and grouped points are assembled into dynamic kernels as inputs to PAConv, thereby increasing the feature dimensionality for each point to capture more informative features. Subsequently, the most significant features are selected as the new feature output through max pooling. In this process, the coordinate space dimension does not change, but the feature space dimension becomes higher, in other words, the number of feature channels increases, allowing more feature information to be obtained and facilitating subsequent accurate feature extraction.  
On the one hand, this network generates the gridded features from the original point cloud with 3D sparse convolution, and projects the downsampled 8\(\times\) feature map to birds-eye view (bew), generating 3D prediction frames, two prediction frames for each pixel and each class, 0\({}^{\circ}\) and 90\({}^{\circ}\), respectively. The keypoints are sampled uniformly from the surrounding area of the proposal via sectorized proposal-centric keypoint sampling (spc). On the other hand, this network determines the nearest neighbors under one radius of each grid point, and then a PointNet++ module embedded in PAConv is used to integrate the features into the features of the grid points. This particular PointNet++ module has more flexibility to better handle irregular and disordered point cloud data. After the point features of all grid points are obtained by means of multi-scale feature fusion, a 256-dimensional proposal feature is obtained through the use of a two-layer multi-layer perceptron (MLP). The above feature information is fused with multi-scale features by the voxel set abstraction module to obtain new multi-scale features. The new features are refined to derive a more accurate 3D prediction box.  
Figure 2: Dynamic network.