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I don't know what camera Google used in that video, but from the look of it, I think it is an RGBD camera - essentially a much better Kinect. Based on the possible number of lenses, it could be RGBD and stereo combined, but I've never seen a sensor like that (the caveat there is that I don't work in industry so I don't get to play with the really really expensive cameras).

1. There is a large range of depth cameras available now. The ASUS Xtion 2 is one recent example. The Orbbec Astra is another often used with ROS. The Intel RealSense is in my opinion one of the best non-industrial ones. For a fuller list, see the sensors page on the wiki

2. If the camera is indeed an RGBD camera then it only needs one camera and one other port for the IR pattern projector. Depending on the application, RGBD can be better than stereo cameras. It's usually also cheaper.

3. A laser scanner could provide much much more accurate depth measurements, but you would need to do calibration with the camera to ensure that you know the exact geometric relationship between the laser and the camera. If you did that, then yes, you could identify selected points on the target through ray projection from the camera and intersection of that ray with the laser data. How accurate this is would depend on how good your laser-camera calibration is and on how high resolution the camera is. Each pixel is effectively a cone of space and the higher the resolution the thinner this cone gets. A larger cone will intersect more rays from the laser.

4. Laser scanners usually provide a LaserScan message. This can be easily converted into a PointCloud2 message.

   Point cloud processing is not my thing, but to the best of my knowledge, PCL provides the best algorithms for processing a point cloud to find a shape. If you want to know if the class you mention will help, you should talk to the lecturer and find out of they plan to teach object matching, object recognition, or similar topics (the wording might vary).