The first variable is what you want to capture. I am trying to capture large outdoor scenes in suitable detail to be used for robotics simulation. A telephoto would not work. I am preferring very wide lenses or even fish eye.
But some people are capturing individual rocks to be used for video game assets, or surveying historical sites or something else. There are times when a telephoto would be desired, especially if you cannot get close to the target scene.
4k is 8.5 megapixels so it’s relatively low detail compared to my 18 megapixel video from my Panasonic. I personally do not really know the math behind any of this so I am not sure where the trade offs are between high resolution rolling shutter or lower resolution global shutter.
Because I am doing scene capture for robot simulation, I do not need a perfectly accurate model. I would rather have a machine learning algorithm that places simple polygons where real world objects are in the video and gives them a reasonable texture. That is, I want to go from video directly to a low poly textured 3D model. My problem is complicated by the fact that I am developing an off road robot, and photogrammetry of forests is very hard.
This is why I mentioned machine learning. I care a great deal about the semantics of my scene and less about metric accuracy. What kills me is compute time. Finding feature points in every image and matching them all together is extremely compute intensive. I believe shortcuts can be discovered for problems like mine with creative neural networks. That said, I still need to learn more about existing algorithms.
But some people are capturing individual rocks to be used for video game assets, or surveying historical sites or something else. There are times when a telephoto would be desired, especially if you cannot get close to the target scene.
4k is 8.5 megapixels so it’s relatively low detail compared to my 18 megapixel video from my Panasonic. I personally do not really know the math behind any of this so I am not sure where the trade offs are between high resolution rolling shutter or lower resolution global shutter.
Because I am doing scene capture for robot simulation, I do not need a perfectly accurate model. I would rather have a machine learning algorithm that places simple polygons where real world objects are in the video and gives them a reasonable texture. That is, I want to go from video directly to a low poly textured 3D model. My problem is complicated by the fact that I am developing an off road robot, and photogrammetry of forests is very hard.
This is why I mentioned machine learning. I care a great deal about the semantics of my scene and less about metric accuracy. What kills me is compute time. Finding feature points in every image and matching them all together is extremely compute intensive. I believe shortcuts can be discovered for problems like mine with creative neural networks. That said, I still need to learn more about existing algorithms.