Toward Target Verification Through 3-D Model-Based Sensor Fusion

Most Automatic Target Recognition (ATR) algorithms operate in 2D image space. Even when using 3D models, these 3D models are typically translated o -line into sets of 2D representations, such as templates, which are then applied to imagery to perform detection, recognition and veri cation. In contrast to this approach, the work reported here takes steps toward direct matching of 3D models to range and optical imagery. The key idea is to exploit known 3D sensor and target geometry to drive a model-based sensor fusion process. This process, which we call `coregistration', resolves uncertainty in the 3D placement of the target relative to the sensors as well as uncertainty in the exact pixel registration between range and optical sensors. A speci c coregistration algorithm is presented along with results on both synthetic and real data. Extending coregistration, two approaches are presented for nding locally optimal matches between 3D target models and multi-sensor data. Both are demonstrated on real data.