We review two different techniques for visualization and processing of three-dimensional (3D) objects based on passive and active optical sensing. First, we describe the basis of a passive-sensing technique based on integral imaging. Also, we show that it is possible to improve the depth of field of this method by using amplitude-modulated microlens arrays. Second, we describe an active-sensing technique based on digital holography. Finally, we apply both techniques to develop 3D image processing applications. In particular, we design two different 3D pattern recognition techniques. Both of them are based in storing the 3D data in two-dimensional (2D) form. In this way, it is possible to recognize 3D objects by performing 2D correlations or applying neural network techniques. Experimental results are presented.