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With the rise of data driven deep neural networks as a realization ofuniversal function approximators, most research on computer vision problems hasmoved away from hand crafted classical image processing algorithms. This papershows that with a well designed algorithm, we are capable of outperformingneural network based methods on the task of depth completion. The proposedalgorithm is simple and fast, runs on the CPU, and relies only on basic imageprocessing operations to perform depth completion of sparse LIDAR depth data.We evaluate our algorithm on the challenging KITTI depth completion benchmark,and at the time of submission, our method ranks first on the KITTI test serveramong all published methods. Furthermore, our algorithm is data independent,requiring no training data to perform the task at hand. The code written inPython will be made publicly available at https://github.com/kujason/ip_basic.

In Defense of Classical Image Processing: Fast Depth Completion on the CPU