Due to the large amount of CSAR echo data carried by UAVs, either the original echo data need to be transmitted to the ground for processing or post-processing must be implemented after the flight. Therefore, it is difficult to use edge computing power such as a UAV onboard computer to implement image processing. The commonly used back projection (BP) algorithm and corresponding improved imaging algorithms require a large amount of computation and have slow imaging speed, which further limits the realization of CSAR 3D imaging on edge nodes. To improve the speed of CSAR 3D imaging, this paper proposes a CSAR 3D imaging method suitable for edge computation. Firstly, the improved Crazy Climber algorithm extracts sine track ridges that represent the amplitude changes in the range-compressed echo. Secondly, two-dimensional (2D) profiles of CSAR with different heights are obtained via inverse Radon transform (IRT). Thirdly, the Hough transform is used to extract the intersection points of the defocused circle along the heights in the X and Y directions. Finally, 3D point cloud extraction is completed through voting screening. In this paper, image detection methods such as ridge extraction, IRT, and Hough transform replace the phase compensation processing of the traditional BP 3D imaging method, which significantly reduces the time of CSAR 3D imaging. The correctness and effectiveness of the proposed method are verified by the 3D imaging results for the simulated data of ideal targets and X-band CSAR outfield flight raw data carried by a small rotor unmanned aerial vehicle (SRUAV). The proposed method provides a new direction for the fast 3D imaging of edge nodes, such as aircraft and small ground terminals. The image can be directly transmitted, which can improve the information transmission efficiency of the Internet of Things (IoT).