Combining Stereo and Time-of-Flight Images with Application to Automatic Plant Phenotyping

This paper shows how stereo and Time-of-Flight (ToF) images can be combined to estimate dense depth maps in order to automate plant phenotyping. We focus on some challenging plant images captured in a glasshouse environment, and show that even the state-of-the-art stereo methods produce unsatisfactory results. By developing a geometric approach which transforms depth information in a ToF image to a localised search range for dense stereo, a global optimisation strategy is adopted for producing smooth and discontinuity-preserving results. Since pixel-by-pixel depth data are unavailable for our images and many other applications, a quantitative method accounting for the surface smoothness and the edge sharpness to evaluate estimation results is proposed. We compare our method with and without ToF against other state-ofthe-art stereo methods, and demonstrate that combining stereo and ToF images gives superior results.