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Use a 600 x 800 input to exercise the height as the normalization scale. The expected horizontal principal point is 379.5 pixels, while main 50e0e3c returns 299.6. All five fixtures fail on main and pass with the patch using the real importer. Ruff 0.12.2 lint and format checks pass. The earlier CI run reports the same 19 Pyright diagnostic locations and messages as PR nerfstudio-project#3797 at the same base. Both Docker runs stop building tiny-cuda-nn because pkg_resources is unavailable. https://github.com/nerfstudio-project/nerfstudio/actions/runs/42401285154 https://github.com/nerfstudio-project/nerfstudio/actions/runs/37900213951 https://github.com/nerfstudio-project/nerfstudio/actions/runs/42401284860 https://github.com/nerfstudio-project/nerfstudio/actions/runs/37900214031
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Convert normalized principal point offsets to pixels using the image's longer side. Perspective and basic fisheye cameras omit the offset fields, so import those as centered. Spherical images have no focal length and need the equirectangular calibration used by the Metashape importer. Preserve the OpenCV fisheye k4 coefficient. Report empty camera lists and unsupported models directly instead of raising IndexError or KeyError.
Added parameterized regression coverage. Exercised the converter in isolation on eight JSON fixtures with NumPy, including portrait-independent scale, missing offsets, spherical and fisheye models, and invalid model descriptions. The full Nerfstudio checks were not run.