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name: geometry-vision description: "Use Kornia geometry and tracking for warps, cameras, epipolar vision, depth, registration, point clouds, and coordinate conventions." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Kornia Geometry Vision

Use this sub-skill when a task mentions Kornia 2D/3D geometry, image warping, resizing, homographies, registration, camera projection/unprojection, calibration, PnP, epipolar pose, triangulation, depth, point clouds, Lie groups, or homography tracking.

Read first

• For exact function families and stable call shapes, read references/api-reference.md.
• For the common off-by-one, axis-order, and matrix-direction traps, read references/coordinate-conventions.md.
• For task recipes, read references/workflows.md.
• For failure diagnosis, read references/troubleshooting.md.
• To verify a local Kornia runtime without downloads, run scripts/geometry_smoke.py.

Fast routing

Choose this sub-skill for:

• resize, rescale, warp_affine, warp_perspective, homography_warp, HomographyWarper, ImageRegistrator, and geometric crop/rotate/scale/shear.
• get_perspective_transform, get_rotation_matrix2d, homography estimation, RANSAC geometry models, and spatial point transforms.
• PinholeCamera, camera intrinsics/extrinsics, project_points, unproject_points, cam2pixel, pixel2cam, stereo, PnP, distortion, epipolar matrices, relative pose, and triangulation.
• depth_to_3d, depth_to_normals, warp_frame_depth, disparity/depth conversion, point-cloud PLY I/O, and So2/So3/Se2/Se3 Lie groups.
• kornia.tracking.HomographyTracker behavior and state transitions.

Common workflows

• Resize or warp an image first, then compare the result with the expected geometry in the same coordinate system.
• When a user gives matched points, confirm whether they are pixel points or normalized camera points before solving anything.
• Use float64 for gradient-check or solver-sensitive work; float32 is the usual runtime default.
• Treat tracking and registration as geometry problems, not augmentation problems.

Pitfalls

• Many geometry bugs are just (h, w) versus (w, h) or source-to-destination versus destination-to-source confusion.
• align_corners affects both resize and warp behavior; do not leave it implicit when reproducing a result.
• Near-zero depth, degenerate point sets, or mixed dtypes can make a correct algorithm look broken.

Quick validation habits

• Test a non-square image and at least one known transform before trusting a warp or registration result.
• Check whether the point coordinates are pixel-space or normalized camera-space before solving.
• Use float64 for solver-sensitive checks and float32 for ordinary runtime warps.
• Keep every matrix and point tensor on the same device as the image tensor.

Route away when the main problem is not geometry:

• Random augmentation containers, AugmentationSequential, masks/boxes/keypoint synchronization: augmentation-pipelines.
• Feature detection, learned matchers, descriptor matching, LoFTR/LightGlue outputs before geometry estimation: features-and-matching.
• Loss or metric choice for training/evaluation: losses-and-metrics.

Operating rules

1. Keep image data as PyTorch tensors, usually B,C,H,W; keep all matrices and point tensors on the same device and dtype as the image or point data.
2. Treat image sizes passed to Kornia geometry as (height, width), not OpenCV's (width, height) convention.
3. Treat pixel points as (x, y) with origin at the top-left; x indexes width and y indexes height.
4. Pass align_corners explicitly when combining resize, grid sampling, homography warping, augmentation matrices, or external geometry references.
5. Avoid half precision for solvers, camera/epipolar math, SVD-heavy paths, and degenerate geometry; prefer float32 for ordinary warps and float64 for precision-sensitive estimation or gradient checks.
6. Do not trigger pretrained-model downloads by default. For tracking, inject already-prepared matchers if model weights are acceptable; otherwise use deterministic low-level homography or RANSAC APIs.