COIFLET BASED METHODS FOR RANGE IMAGE PROCESSING

In industry applications, the range images are generally huge points arrays and are additively noised. They usually represent surfaces of 3D objects and are used for reverse engineering process in CAD/CAM domains. To compute the geometrical model of each surface present in the range image, we denoise and sub-sample the raw range data. Denoising allows us to avoid the adverse effects of the noise on the obtained result. Sub-sampling the raw range data leads to a low image processing overheads like those of segmentation process. Based on interpolation properties of particular wavelets named coiflets, we propose a method for smoothing noisy range images. The smoothed image keeps invariant the "topological characteristics" of the represented surfaces. Thereafter, we propose a method for sub-sampling dense range images which leads to the reduction of the amount of raw data by a factor of four. This method eliminates the "redundant" information, thus the obtained result describes the essential details (as the shape of the physical surface) of the initial range image. The smoothing and sub-sampling methods are designed to be easily integrated in any reconstruction algorithm to improve its result and reduce its overhead in spite of its high complexity.