Comments (1)
To answer one of my questions, it's fov_y = 2 * std::atan(0.5 * image_height / f_y) if the camera poses are not optimised.
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Related Issues (20)
- Pytorch and cuda getting automatically upgraded during docker build
- Install tiny-cuda-nn/gsplat
- Bad loss spike when training nerfacto with high number of iterations HOT 1
- Cannot access viewer from local machine HOT 2
- The K4 intrinsic parameter in nerfstudio doesn't match OpenCV or COLMAP
- Installation guide should mention that visual studio 17.10 doesnt work
- Colmap unable to find poses for custom datasets HOT 1
- Optional config argument to pause training at the very beginning.
- Nerfacto could not render rgb when disabling the scene contraction HOT 3
- Nerfstudio not converging after 17000+ steps - scene still blurry HOT 5
- for mip nerf viewer is not getting opened HOT 1
- Unable to use custom data from Metashape/Reality Capture HOT 1
- weird moire pattern when training instant-ngp without scene contraction HOT 1
- Installation via Pixi fails on Ubuntu 24.04 HOT 2
- Adjusting render position slightly with ns-render
- ExportGaussianSplat produces an invalid ply file when sh_degree == 0
- Exported nerfacto point cloud has huge skybox surrounding it HOT 6
- AttributeError: 'RandomCamerasDataManager' object has no attribute 'train_dataparser_outputs' HOT 1
- Green tint with splatfacto-big and bilagrad HOT 3
- No scene option in viewer HOT 2
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