Choose sampling and fusion

Level: Compose · Strategy: Projection-domain

Sampling decides where pinhole views look and how much they overlap. Fusion decides how several valid predictions contribute to one spherical direction. These choices are independent of the face-local model.

• Use cube for six conventional views.

• Use icosahedron or Fibonacci for more uniform angular coverage.

• Increase overlap when the method is unreliable near face boundaries.

• Use continuous blends for RGB, logits, confidence or range.

• Use nearest sampling and closest fusion for labels and masks.

FOV controls distortion and context: a narrow view is locally more pinhole-like but requires more faces; a wide view gives the model more context but increases projection distortion. Record layout, FOV, face size, overlap and blend with every result.

Choose from the output, not from the picture

Output	Face sampling	Reconstruction/fusion
RGB, float response, logits, CAM	bilinear when valid	feather, Gaussian or weighted mean
radial range	bilinear plus explicit validity	validity-aware continuous blend
label image or boolean mask	nearest only	closest/discrete policy
keypoints	detector-native pixels	bearing lift plus angular NMS
boxes, polygons, masks	geometry-preserving lift	solid-angle IoU NMS or merge
class scores/embedding per face	one record per supported footprint	coverage-normalized aggregation

The face size controls the planar method’s sampling density. The FOV controls angular context and distortion. The layout controls directional coverage. Changing all three at once makes comparisons uninterpretable, so record them independently.

Practical selection

Start with cube when interoperability with a conventional cubemap matters. Use icosahedron for broadly uniform overlapping views. Use Fibonacci directions when the experiment requires an explicit number of approximately uniform centres. Then inspect the support map: a nominal list of views is not proof that every source direction contributed equally.

Fusion must match the quantity. Gaussian weighting can reduce continuous border artifacts, but it must never average class IDs. closest can preserve discrete values, but it does not make a low-quality boundary correct.

Reproducibility record

Store the sampler name and parameters, face shape, horizontal/vertical FOV, interpolation, support policy, blender and output units. These values are part of the method, not presentation metadata.

See the sampler and blender reference for every supported policy and the projection foundations for the ray geometry. Next: choose the output contract.