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Pose Distribution Prior #2
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Really good questions! Many scenes can be categorized into three cases: forward-facing, inward-facing, and randomly distributed scenes. For forward-facing scenes, the camera poses can be directly optimized via gradient descent, so we don't have to find a prior distribution. For inward-facing scenes, the prior distribution of many scenes can be roughly represented with cameras position distributed uniformly on a sphere surface or in a spherical shell, and lookat point around the origin. Then, we have to tune the parameters of the region of the sphere radius, azimuth, elevation, lookat point. For randomly distributed camera poses, like in a room, it's hard to define the distribution with a few parameters, we may need other poses estimation tools to determine a rough camera region for each image, then we can set the prior distribution as the union of all regions. |
Dear Authors,
Thanks for the interesting work and releasing the code.
I was wondering about the advice that you've put in the readme on training with our own data.
Assuming that I only have an image dataset, how can I 1) find this suitable prior distribution, 2) train your model on it?
Thanks for your help in advance.
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