Multi-scale feature extraction has become prominent in recent years. Additionally, processing images containing sparse or irregularly distributed data has become increasingly important, in particular with respect to the use of range image data. We present a family of multi-scale gradient-based edge detection algorithms that are suitable for use on either regularly or irregularly distributed image data; these algorithms can be applied directly to the range and intensity images without any image pre-processing. We quantitatively evaluate our algorithms on synthetic intensity and range images and also provide comparative visual output, using real images. The results demonstrate that this approach can be successfully applied to both range and intensity images, providing results that for intensity images are more accurate than from traditional gradient operators and for range images are more accurate than from the scan-line approximation.