Efficient Photometric Stereo Using Kernel Regression

Photometric stereo estimates surface normals from multiple images captured under different light directions using a fixed camera. To deal with non-Lambertian reflections, the recent photometric stereo methods employ iterative or optimization frameworks that are computationally expensive. This paper proposes an efficient photometric stereo method using kernel regression, which can be transformed to an eigendecomposition problem. The kernel parameter is variable for each surface point so that it can cope with the variety of general reflectances. The best kernel parameter is automatically determined by leave-one-out cross validation. To improve computational efficiency, the leave-one-out process is accelerated by fast matrix computation and proper normal initialization. The proposed photometric stereo method is extensively evaluated on synthetic and real surfaces with various reflectances. Experimental results validate that the method is computationally efficient and achieves the state-of-the-art accuracy in surface normal estimation.