We present a feature-selective segmentation and merging technique to achieve spatially resolved surface profiles of the parts by 3D stereoscopy and strobo-stereoscopy. A pair of vision cameras capture images of the parts at different angles, and 3D stereoscopic images can be reconstructed. Conventional filtering processes of the 3D images involve data loss and lower the spatial resolution of the image. In this study, the 3D reconstructed image was spatially resolved by automatically recognizing and segmenting the features on the raw images, locally and adaptively applying super-resolution algorithm to the segmented images based on the classified features, and then merging those filtered segments. Here, the features are transformed into masks that selectively separate the features and background images for segmentation. The experimental results were compared with those of conventional filtering methods by using Gaussian filters and bandpass filters in terms of spatial frequency and profile accuracy. As a result, the selective feature segmentation technique was capable of spatially resolved 3D stereoscopic imaging while preserving imaging features.