## 1 Introduction  
Ground-Based Synthetic Aperture Radar (GBSAR) is a new SAR measurement technology that enables acquiring SAR data via a fixed length of linear track [1; 2; 3]. Its non-contact, all-weather and flexible stationing capabilities offer advantages over traditional spaceborne and airborne platforms, making it more flexible and cost-effective for several applications. With its high precision of repeated path SAR data acquisition, GBSAR has found its usage in monitoring small deformations of landslides, bridges, and buildings [4; 5; 6]. However, the current linear track GBSAR can only generate two-dimensional (2D) SAR images, as the layover effect means it cannot retrieve 3D deformation information [7; 8]. Circular Scanning Ground-Based Synthetic Aperture Radar (CS-GBSAR) is a novel system of GBSAR, which adopts an airborne circular SAR mode into the ground-based platform. It creates a 2D circular synthetic aperture with the antenna rotating on the vertical plane, and makes it capable of 3D imaging on buildings or other scenes with changing altitude [9; 10; 11; 12]. However, the curved observation geometry of CS-GBSAR causes the strong sidelobes in 3D images. As a result, the strong sidelobe diffuses throughout the 3D SAR data, which interferes with adjacent weak targets' main lobe at different heights. This interference leads to the loss of target detection, thereby causing difficulty in accurately extracting target point clouds in 3D SAR images. Currently, to the best of our knowledge, there exists no effective method for extracting target point clouds from 3D SAR images. One feasible idea to obtain the point cloud is first using a Constant False Alarm Rate (CFAR) detector at different heights to obtain potential point clouds, then extract the exact points with a clustering method such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN). CFAR has an adaptive threshold detection ability and can adapt to changes in background clutter [13]. It is first applied to detect targets in range profiles. Later, Lincoln Laboratory extended the Two-Parameter CFAR detector to process SAR images in two dimensions [14; 15; 16], it is now a relatively matured in 2D SAR images. While implementing the two-parameter CFAR algorithm for target detection, it is essential to detect each layer within the 3D image at varying altitudes. Unfortunately, the diffused 3D strong sidelobes have an influence on detection results, leading to many false detections. To extract point clouds and suppress false detections, the widely applied DBSCAN method can be adopted [17; 18; 19; 20]. Utilizing this algorithm has numerous advantages, including generating cluster for any shape and effectively eliminating outliers in data. However as shown in this paper, the conventional DBSCAN using a spherical template has poor clustering performance. To accurate extract points and exclude the false detection of the sidelobes, the template's shape should consider the data's own unique characteristics. To deal above issues in the extraction of 3D point clouds from CS-GBSAR, this paper proposes a new two-step CFAR-based 3D point cloud extraction method. It is a general method that can handle the CS-GBSAR's data processing in 3D point cloud extraction applications. It first utilizes maximum projection to obtain three-view images from 3D image data. Then, the first step Conventional Cell Averaging Constant False Alarm Rate (CA-CFAR) is applied to obtain the coarse masks of the three-views. Then, the volume mask in the original 3D image is obtained via inverse projection. This can remove strong sidelobes outside the potential target region and obtain potential target area data by intersecting it with the SAR 3D image. Then, the second step CA-CFAR is applied to potential target area data to obtain 3D point clouds. Finally, to further eliminate the residual strong sidelobes and output accurate 3D point clouds, the modified DBSCAN clustering algorithm is applied.