Depth compression processing for three-dimensional display content is an effective solution to the problem of insufficient depth range in 3D raster displays. However, commonly used compression methods inevitably cause geometric deformation in the main subjects of the scene. This paper proposes a depth optimization method for dense viewpoint3D raster displays based on 3D point clouds. By reconstructing the 3D point cloud from the stereo disparity map of the 3D scene, the main subject’?s point cloud is segmented, and depth position adjustments are made only for subjects exceeding the display’?s depth range, thereby maintaining the geometric structure of the scene’?s main subjects. This achieves overall depth compression of the 3D scene without altering the geometric integrity of the main subjects. Through a series of statistical experiments focused on the visual experience, the proposed method received an approval rating exceeding 77% from each participant and surpassed 80% for every experimental scenario. The results affirm the method’?s significant enhancement of the audience’?s subjective perception, compressing the overall depth of the scene while ensuring no deformation of the main subjects, thus providing viewers with a natural viewing experience and strong depth perception of the scene’?s main subjects.