I think floating point is accurate as long as you don't have lots of significant figures. eg. 0.0000000000000001 is accurate, but 0.57283847562637473 isn't because you have to store lots of figures. This is because floats are stored as a value and how many decimal places it is left/right of the decimal point.
As such, 1000.00 metres and 0.001 metres can be represented perfectly accurately. When you hear of accuracy problems with flaoting point, it's because you can only store so many significant figures. eg. If a division x/a results in the real answer 547.348571834568134581734581733854791356481 internally this would be rounded to maybe 547.34857183456813452. The actual accuracy is 2^32. Lots of roundings results in float inaccuracies.
But storing figures is likely to be accurate for the users anyone would want in setting a scene.
As such, 1000.00 metres and 0.001 metres can be represented perfectly accurately. When you hear of accuracy problems with flaoting point, it's because you can only store so many significant figures. eg. If a division x/a results in the real answer 547.348571834568134581734581733854791356481 internally this would be rounded to maybe 547.34857183456813452. The actual accuracy is 2^32. Lots of roundings results in float inaccuracies.
But storing figures is likely to be accurate for the users anyone would want in setting a scene.