Hi! Thank you for the great work! Your real-time results look amazing!
I have a question about the depth image data. It seems like in all of your reconstructed results, the backgrounds (for example the walls) are all removed. May I ask how you guys do it? I mean, it seems like optical flow can solve part of the problem. But optical flows are generated from color images, right? I assume they are not perfect. For example, if you pick all the points that have flow values u^2+v^2 > 1, there will always be some background pixels included in the masked area. Do you set up a threshold for input depth values so you subtract the background in the very beginning? Or do you remove it after you calculated the optical flow? Or do you build everything in the canonical model anyway, you just not visualize it in the experiment? Or anything else?
In some other cases, the person may not move very drastically, so the optical flow may ignore a large part of the person. Do you run into similar problems? Any idea how I can solve this?
Hi! Thank you for the great work! Your real-time results look amazing!
I have a question about the depth image data. It seems like in all of your reconstructed results, the backgrounds (for example the walls) are all removed. May I ask how you guys do it? I mean, it seems like optical flow can solve part of the problem. But optical flows are generated from color images, right? I assume they are not perfect. For example, if you pick all the points that have flow values u^2+v^2 > 1, there will always be some background pixels included in the masked area. Do you set up a threshold for input depth values so you subtract the background in the very beginning? Or do you remove it after you calculated the optical flow? Or do you build everything in the canonical model anyway, you just not visualize it in the experiment? Or anything else?
In some other cases, the person may not move very drastically, so the optical flow may ignore a large part of the person. Do you run into similar problems? Any idea how I can solve this?
Thanks again.