A novel multi-modal scene segmentation algorithm for obstacle identification and masking is presented in this work. A co-registered data set is generated from monocular camera and light detection and ranging (LIDAR) sensors. This calibrated data enables 3D scene information to be mapped to time-synchronized 2D camera images, where discontinuities in the ranging data indicate the increased likelihood of obstacle edges. Applications include Advanced Driver Assistance Systems (ADAS) which address lane-departure, pedestrian protection and collision avoidance and require both high-quality image segmentation and computational efficiency. Simulated and experimental results that demonstrate system performance are presented.