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There are a lot of ways, but a Kalman filter is the easiest. Set up a 3d kalman filter with either Position, Position and Velocity, or Position Velocity and Acceleration, depending on what the point is doing.
Your key frame's uncertainty is the error cov Pre and post initialization, and the position is the state pre/post initialization. Then each new position is the measurement, and new uncertainty is the measurement covariance.
By the end of a sequence, the kalman error should be smaller than your measurement error.
HERE is an example of somebody using a 2d position and velocity one. It should be enough to get you started. Googling Kalman Filter will give lots of class lectures and things if you want the full theory.
