Tomographic SAR is a technique that allows to extend synthetic aperture radar (SAR) imaging to the third dimension by using several images of a scene acquired from different sensor positions. Three-dimensional point clouds extracted thanks to tomographic processing methods are often corrupted by noise and artifacts that need to be corrected. In this paper, we propose a simple convex optimization formulation that exploits the geometric constraint that the line of sight between a sensor and a surface measurement must not be blocked by another surface. We demonstrate the ability of our method to denoise point clouds and fill holes on both synthetic data and experimental E-SAR data by the German Aerospace Center (DLR).