DarkRoom/docs/panorama.md

Panorama

Status: Draft · 2026-09-19 Companion to: requirements.md §3.11 FR-MRG-1 … 11, D18, S15 · architecture.md §5.2, §6.2

The first merge (§3.11): several frames, rotated about one point, become one photograph. This document is how that lands on the pipeline that exists now — which stages, where each runs, how the composite is produced in chunks when it is larger than any texture or any memory, what is ported from where, and what the keypoint model may be under D8.

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1. Why it is worth the work

The audience shoots panoramas and leaves the application to stitch them. That is the same workflow break dust was (spot-removal.md §1): a RAW editor that does everything but the one thing, and the photographer's work ends up in a JPEG produced by a tool that never saw the RAW.

It is also the merge whose alignment problem is smallest. A panorama is a rotation — three parameters per frame plus a focal length — with no depth to recover. HDR merge and focus stacking share its data model (D18) and most of its machinery (FR-MRG-3, 5, 6, 7, 10, 11 are written to be general); building the panorama first builds the shared part on the easiest geometry.

2. Non-goals

• Not structure-from-motion. No translation is solved for. A hand-held set with parallax gets its ghosts hidden by seam placement, and a set with real parallax is not a panorama. COLMAP's front end is the right mental model; its back end is the wrong problem.
• Not a multi-source Version. D18. The composite is a file, and nothing in the catalog, the sidecar format or sync learns about cross-references.
• Not boundary fill. Painting pixels that were never captured is the pixel editing §1.3 excludes. Auto-crop is the tool.
• Not automatic. The tool proposes an alignment and writes nothing until the photographer confirms. Same rule as spot removal and D17, for the same reason: a merge that silently omits or misplaces a frame is the failure this application must not have.
• Not HDR-panorama in one pass. Until HDR merge exists on its own, a bracketed panorama is bracketed frames merged first, then stitched.

3. What is new, precisely

Nearly all of it, unlike spot removal. The pipeline renders one source to one texture; nothing in the tree detects keypoints, estimates a rotation, warps into a projection, finds a seam, or blends a pyramid. What exists and is reused:

Exists	Where	Reused for
Render a source to scene-linear on the GPU	dr-gpu demosaic → camera profile → working space	FR-MRG-2's input, once a tap after lens correction and before tone exists (S15.3)
Tiled rendering with a priority scheduler	ARCH §5.3	Pulling source tiles on demand into an output chunk (§5 below)
A non-CFA source entering the pipeline	Demosaicer::from_rgba8	The composite's decode path, if the container is a TIFF (S15.1)
DNG matrices read through rawler	dr-decode::profile	The composite's decode path, if the container is a DNG
Static-shape ONNX under tract, heads decoded in Rust	dr-segment	The keypoint model (§6)
A batch worker with its own GpuContext, activity row, cancel	dr-ui::export	FR-MRG-7 verbatim
The 16-bit TIFF encoder with metadata sub-IFDs	dr-export::encode	FR-MRG-3's writer, extended to linear samples
Multi-select in the grid	collections_ui::selected	The entry point

New: a core/dr-pano crate holding the geometry (keypoints, matching, the rotation solve), a set of WGSL passes in dr-gpu (reprojection, gain, seam, pyramid blend), the chunked output driver, the container writer, and the dialog.

4. The stages, and where each runs

FR-MRG-10 states the rule; this is the table it was written from.

Stage	Cost shape	Runs on	Why
Source to scene-linear	per pixel, full res	GPU, the existing pipeline	It is the pipeline
Keypoint detection	once per frame, at 1024 px	CPU, tract (NEON on the tablet)	Bounded by frame count, not output size. Same runtime faces and masks use. Hand-written WGSL convolutions for a model that runs five times would be work with no visible gain.
Descriptor matching	K² × D per pair	CPU, SIMD	2048² × 64 × 10 pairs ≈ 3 GFLOP — tens of milliseconds
Rotation solve, bundle adjustment	3N + 1 parameters, Levenberg–Marquardt	CPU	Microseconds. Not parallel work.
Preview reprojection	per pixel, proxy res	GPU, interactive	Projection and horizon changes re-warp N proxies at frame rate
Full-resolution warp	per output pixel	GPU, chunked (§5)	The heaviest thing in the application
Gain compensation	per overlap region	GPU reduction, then N scalars	Sums, on the histogram pass's pattern (ARCH §5.5)
Seam finding	per overlap pixel	GPU-friendly variant	Graph cut resists the GPU; a distance-transform or per-column DP seam does not. The algorithm is chosen for the GPU, not for the paper.
Multi-band blend	per pixel × levels	GPU, chunked	Laplacian pyramids are separable convolutions — the detail stage's shape
Encode	per pixel, once	CPU, streamed per chunk row	As export does

5. Chunked in output space

FR-MRG-11 forbids holding the composite as one texture, and two facts force it before memory does:

• max_texture_dimension_2d is 8192 on many mobile GPUs and 16384 on desktop. A three-row panorama is routinely 20 000 px wide.
• Five 24 MP frames at working precision are ~1 GB together. The tablet does not have it.

The geometry is known before any full-resolution pixel exists. Alignment runs on proxies; what comes out is a rotation per frame, a focal length, a projection and an output rectangle. From those, every output pixel's source coordinates in every frame are a closed-form function. That is what makes chunking simple rather than clever:

for each output chunk C (e.g. 2048 × 2048, in output space):
    frames_in(C) = frames whose projected footprint intersects C
    for each frame F in frames_in(C):
        source tiles T(F, C) = tiles of F that project into C, plus a margin
        render T(F, C) to scene-linear through the pipeline's tile cache
        warp T(F, C) into C's coordinate frame           ← GPU
    gain-correct, seam, blend within C                     ← GPU, with overlap
    read C back, encode its rows                           ← CPU, streamed

The working set is one chunk, its per-frame warped copies, and the source tiles that fed them. It does not grow with the composite.

The blend needs a margin. A Laplacian pyramid of L levels reads 2^L pixels beyond the chunk edge; a chunk is therefore rendered with a margin of that width and the margin discarded after the blend. Seams cross chunk boundaries and must agree on both sides: the seam is found once at a reduced resolution over the whole overlap (which fits — it is a mask, not an image), then upsampled into each chunk. The same is true of gain: the scalars are solved once from proxy-resolution overlaps and applied everywhere.

Source tiles are the pipeline's tiles. ARCH §5.3's cache keys by (VersionId, tile, zoom, graph_hash_prefix); the merge asks for tiles of a neutral graph at zoom 1 and gets the same caching every other consumer does. A tile pulled for one chunk is usually needed by the neighbouring chunk, and stays hot for it.

6. The keypoint model

FR-MRG-8: works without weights, better with them. The licence read comes first (D13's lesson, S15.2).

Model	Licence	Fits tract?	Position
XFeat (CVPR 2024)	Apache-2.0	Plain convolutions, fully convolutional, the repo ships an ONNX export	Chosen. Fixed 1024 px input, dense heatmap and descriptor map out, NMS and top-K in Rust — the yolo26 pattern
DISK	Apache-2.0	U-Net, static	Second choice; stronger descriptors, ~3–4× the compute
ALIKE	BSD-3	Plain convolutions	Fallback if XFeat's export fails F6
ALIKED	BSD-3	Deformable convolution in the descriptor head	Unlikely to load
SuperPoint, SuperGlue, R2D2, SiLK, MASt3R	non-commercial	—	Out on licence
LightGlue	Apache-2.0	Transformer over a variable keypoint count	Not until mutual-nearest-neighbour matching fails on a real set

Without weights: AKAZE (BSD, akaze from rust-cv), which is adequate on well-textured overlaps and worse on sky, repeated structure and exposure drift — which is where a learned detector earns its place.

Matching is mutual nearest neighbour with a ratio test, then RANSAC on a rotation model. For a panorama — one lens, near-pure rotation, 20–40 % overlap — that is what Hugin and OpenCV's stitcher use, and it is enough.

7. What is ported from where

Nothing is linked; everything is read.

Source	Licence	Taken
OpenCV modules/stitching	Apache-2.0	The stage layout — Brown & Lowe (2007) as a set of small classes with one job each — and the warpers' projection maths
OpenPano (ppwwyyxx)	MIT (verify on read)	The estimation and bundle-adjustment maths, function by function, with outputs diffed against it
enblend-enfuse	GPLv2+	Seam-line optimisation and Burt–Adelson multi-band blending
Hugin nona	GPLv2+	The GLSL remapper, as the reference for the WGSL warp

The golden set (§8 of the requirements) is OpenCV's stitcher on the same inputs: a reference output to compare against, within a tolerance calibrated the way S9 calibrates R1.

8. The output file

FR-MRG-3. Scene-linear, ≥ 16 bits, wide gamut, the first source's capture metadata, named from the first source with a -pano suffix, beside it.

Two containers are candidates and S15.1 decides:

• Linear DNG. PhotometricInterpretation = LinearRaw, three samples per pixel, ColorMatrix1 carried from the first source. Re-enters through rawler as Format::Dng with no new decode path, if rawler reads it back. What Lightroom writes.
• Float TIFF. SampleFormat = IEEEFP, 16 or 32 bits, an ICC profile for the working space. Needs Format::Tiff and a decode path, but the writer is the existing encoder with a different sample type, and nothing about it is uncertain.

Either way the composite enters the pipeline as a non-CFA, linear source — from_rgba8's sibling with non_linear = false and the base curve resolved from the carried matrix — and is developed as any RAW is.

9. Interaction

• The entry is the grid's selection: two or more images, one action, "Merge to panorama". One image, or images from different roots, and the action says why it is unavailable.
• The dialog shows the aligned proxies in the chosen projection, with the projection, horizon and crop controls of FR-MRG-4, and the per-frame residuals. A frame that failed to align is named there (FR-MRG-5), and the merge cannot be confirmed with it in the set.
• Confirm starts the FR-MRG-7 job. The composite appears in the grid when the file is written and catalogued, beside its sources, with the merge as the first entry in its history.

10. Order of work

1. S15, all four, before anything else. (1) and (2) are a day each and either can change the design.
2. dr-pano: keypoints (AKAZE first, XFeat when S15.2 passes), matching, RANSAC, rotation solve. Unit-tested against synthetic rotations of one frame, where the answer is known exactly.
3. The working-space tap, and the preview reprojection pass. At this point the dialog can show an alignment.
4. The chunked driver with a feathered blend — the whole path end to end, writing a file, before the blend is good.
5. Gain, seams, multi-band.
6. The container, the catalog entry, provenance, the history entry.
7. Tablet: NFR-MRG-1's figure, and FR-MRG-9's ceiling.