Applications catalog

Applications are immutable, task-level wrappers. They accept the natural inputs of a task, expose parameters through get_params() and with_params(), describe their implementation, and reveal their lower-level composition through as_pipeline(). Start here unless you need to replace one of those internal decisions.

Registered Application surfaces

This table is generated from the public documentation contract. A new Application cannot disappear from the catalog merely because a hand-written table was not updated.

Surface	Stability	Canonical import	Contract	Public components or entries
panorama-stitching-application	Experimental	panorai.applications	panorai-panorama-stitching/v1-experimental	PanoramaStitcher
registered-full-sphere-stereo-application	Experimental	panorai.applications	panorai-registered-full-sphere-stereo/v1-experimental	FullSphereDenseStereo
spherical-applications-dog-reviewed-profile	Experimental	panorai.applications	panorai-spherical-dog-features/v1	SphericalDoGFeatures.reviewed_profile
spherical-applications	Experimental	panorai.applications	panorai-application/v1-experimental	Application, ApplicationConfigurationError, ADCensusStereoDepth, AssessRelativePoseCapture, CensusMGMStereoDepth, ELASStereoDepth, PipelineApplication, PatchMatchStereoDepth, PanoramicDenseStereo, InstanceHeuristicDecision, RelativePoseCaptureAssessment, RelativePoseCaptureError, RelativePoseCaptureInput, RelativePoseCaptureMetadata, RelativePoseCapturePolicy, RelativePoseProfile, SphericalDenseReconstruction, SphericalDoGFeatures, SphericalMaskRCNNPaper, SphericalMaskRCNNWorkflow, SphericalRelativePose, SphericalStereoDepth, SphericalStereoInput, StereoBMDepth, StereoSGBMDepth
spherical-applications-slic	Stable	panorai.applications	panorai-spherical-slic/v1	SphericalSLIC
spherical-applications-auto-experimental	Experimental	panorai.applications	panorai-application/auto-experimental	—

Concrete applications

Public task wrappers

Application	Stability	Input	Output	Domain	Exposed composition
panorai.applications.SphericalSLIC	Stable	one canonical ERP with explicit validity	RasterField(kind="labels")	direct sphere	OnSphere(Output(SLIC(), kind="labels"))
panorai.applications.SphericalDoGFeatures	Experimental	one canonical ERP	SphericalFeatureSet with tangent descriptors	sphere + tangent patches	one task Stage using SphericalDoGSIFTExtractor
panorai.applications.SphericalRelativePose	Experimental	ordered reference/source ERPs; optional capture metadata	RelativePoseResult or None	features → bearings → spherical pose	capture assessment, extraction, matching, bearing conversion, pose estimate
panorai.applications.SphericalStereoDepth	Experimental	two ERPs plus metric source-from-reference pose	SphericalStereoResult with radial range, confidence and validity	direct-spherical plane sweep	one typed stereo Stage over SphericalDenseStereo
panorai.applications.SphericalDenseReconstruction	Experimental	registered central ERPs with supplied metric world poses	accepted/rejected result with mesh, normals, evidence and optional color	multireference MVS → world TSDF → surface	six public Stages from reference-set construction through validation
panorai.applications.SphericalMaskRCNNWorkflow	Experimental	one ERP plus configured CAM/SAM adapters and instance heuristic	SphericalSegmentationResult	spherical evidence + SAM masks	CAM or SAM proposals, mask refinement/fusion, heuristic BoxHead
panorai.applications.PanoramaStitcher	Experimental	\(N \geq 2\) overlapping planar pinhole images; optional support, validity and horizontal FOV	partial EquirectangularImage, recovered rotations/intrinsics, contribution maps and diagnostics	planar evidence → central SO(3) registration → ERP fusion	feature detection, overlap discovery, calibration/registration, warp, exposure compensation and blend

SphericalMaskRCNNPaper remains an import-compatible alias for the original Experimental name. mask_rcnn.paper() delegates to the canonical mask_rcnn.workflow() factory with a deprecation warning.

All wrappers run directly with application(...) or application.run(...). as_pipeline() is the supported escape hatch: inspect it, retain intermediate results, or reuse individual public Stages. Internal adapter Stages whose names start with _ are implementation details and are not catalog entries.

DoG reviewed-profile candidate

SphericalDoGFeatures.reviewed_profile() is inventoried separately as panorai-spherical-dog-features/v1. It freezes the dense spherical DoG detector, tangent-patch support rules and RootSIFT descriptor. Changing a scientific parameter with with_params() deliberately returns the generic Experimental wrapper and removes the profile identity. Selecting numpy, native or auto convolution changes only the execution backend and preserves the identity.

The named profile is still Experimental. Its separate catalog row describes the remaining scene-disjoint external evaluation, installed-wheel performance, Python/platform matrix and independent-review gates. The lower-level generic DoG detector, coarse-to-fine detector and free-form application configuration do not inherit a future promotion of this factory.

Framework contracts

Application framework

Component	Stability	Responsibility
panorai.applications.Application	Experimental	Common call, parameter, description and safe-configuration contract.
panorai.applications.PipelineApplication	Experimental	Application whose executable lower-level form is a public Pipeline.
panorai.applications.RelativePoseProfile	Experimental	Named, immutable bundle of frontend, estimator, sampler and acceptance policy.
panorai.applications.RelativePoseCapturePolicy	Experimental	Pre-estimation capture eligibility; it does not estimate pose or guarantee success.

Persistence is available only when every nested component has a registered, safe representation. In-memory callables remain executable but are not made serializable by the wrapper. No application silently downloads weights or accepts third-party terms.

Follow the Applications tutorial, read the wrapper concept, or use the signature-level Applications API.