3D face modeling is currently a popular area in Computer Graphics and Computer Vision. Many techniques have been introduced for this purpose, such as using one or more cameras, 3D scanners, and many other systems of sophisticated hardware with related software. But the main goal is to find a good balance between visual reality and the cost of the system. In this thesis, reconstruction of a 3D human face from a pair of stereo cameras is studied. Unlike many other systems, facial feature points are obtained automatically from two photographs with the help of a dot pattern projected on the object&amp / #8217 / s face. It is seen that using projection pattern also provided enough feature points to derive 3D face roughly. These points are then used to fit a generic face mesh for a more realistic model. To cover this 3D model, a single texture image is generated from the initial stereo photographs.

<p>A Velodyne Puck VLP-16 LiDAR and a Carnegie Robotics Multisense S21 stereo camera were placed in an environmental testing chamber to investigate dust and lighting effects on depth returns. The environmental testing chamber was designed and built with varied lighting conditions with corn dust plumes forming the atmosphere. Specific software employing ROS, Python, and OpenCV were written for point cloud streaming and publishing. Dust chamber results showed while dust effects were present in point clouds produced by both instruments, the stereo camera was able to “see” the far wall of the chamber and did not image the dust plume, unlike the LiDAR sensor. The stereo camera was also set up to measure the volume of total mixed ration (TMR) and shelled grain in various volume scenarios with mixed surface terrains. Calculations for finding actual pixel area based on depth were utilized along with a volume formula exploiting the depth capability of the stereo camera for the results. Resulting accuracy was good for a target of 8 liters of shelled corn with final values between 6.8 and 8.3 liters from three varied surface scenarios. Lessons learned from the chamber and volume measurements were applied to loading large grain vessels being filled from a 750-bushel grain cart in the form of calculating the volume of corn grain and tracking the location of the vessel in near real time. Segmentation, masking, and template matching were the primary software tools used within ROS, OpenCV, and Python. The S21 was the center hardware piece. Resulting video and images show some lag between depth and color images, dust blocking depth pixels, and template matching misses. However, results were sufficient to show proof of concept of tracking and volume estimation. </p>