# FANet: Improving 3D Object Detection with Position Adaptation
## 2 Related Work  
In terms of three-dimensional object detection with voxel-based methods [4; 6; 7; 8; 9; 10; 11], VoxelNet [12] divides point clouds into a certain number of voxels according to the ratio of length \(\times\) width \(\times\) height and divides points in point cloud space into corresponding voxels of location. It uses several individual feature coding layers for local feature extraction for each non-empty voxel. However, ignoring the sparse distribution of point cloud data in space wastes a lot of computation. [PERSON] [13] introduced new angular loss regression by improving the sparse convolution method, which reduces the disadvantages of voxel-based network models in terms of irregularity and disorder. Based on SECOND, SA-SSD [14] to preserve structure information is proposed. Dense voxel fusion [10] is a sequential fusion method that generates multi-scale dense voxel feature representations, improving the expressiveness in low-point-density regions. CenterPoint [15] uses key point detectors and point feature regression to represent, detect, and track 3D objects. Based on a framework of dynamic graphs, DGCNN [16] uses key point detectors on point clouds and point feature regression to represent and detect 3D objects and proposes an ensemble-to-set knowledge distillation method applicable to 3D detection. This method produces more efficient 3D proposal boxes; however, important geometric information may be lost due to quantization [17].  
In terms of three-dimensional object detection with point-based approach [9; 18; 19], [PERSON] designed early work called PointNet [5] based on learning point cloud representations, which is simple and effective. Many subsequent point-based 3D object detection networks have been developed based on PointNet. Transform operations and maximum pooling in PointNet can effectively extract the global features of points, but local features cannot be obtained. PointNet++ [19] proposed sampling and grouping to improve local feature extraction for PointNet. In order to further reduce the inference time, 3D-SSD [20] uses only the backbone for downsampling feature extraction to complete the detection task. SE-SSD [21] includes a pair of teacher and student SSDs, contains an efficient IoU-based matching strategy to filter the teacher's soft targets, and formulates a consistency loss to align the student's predictions with it. Pointformer [22] uses local and global transformer modules to model dependencies between points. 3D-CenterNet [23] can preferentially estimate the center of an object from a point cloud. This approach provides flexible receptive fields for point cloud feature learning [24], however, which makes it difficult to express the complex variability in the point cloud space [25].  
PV-RCNN [26], which is a two-stage 3D point cloud object detection method, mainly adopts a two-step strategy of point-voxel feature aggregation for accurate 3D object detection. This combines the advantages of both point-based and voxel-based methods to obtain multi-scale feature information, including multi-scale voxel semantic information and the location information of points. The voxel set abstraction module in PV-RCNN draws upon the concept in PointNet++, which is theoretically successful, but in practice has also traded off its design flexibility for effectiveness [27], resulting in poor object detection accuracy [28].  
PAConv [29] builds a convolution kernel from a basic weight matrix, whose combined coefficients are adaptively learned by LearnNet from relative points. PAConv is flexible enough to accurately model complex spatial variations and geometric structures in 3D point clouds. However, PAConv itself does not have object detection capability. Therefore, this paper forms a new 3D object detector by embedding PAConv into the PointNet++ module. The detector offers not only point-based and voxel-based advantages, such as its provision of flexible acceptance domains and efficient 3D proposal boxes, but also has good object detection accuracy.