DarkRoom/docs/dev/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 through the fused pass, with a linear f16 output mode	dr-gpu demosaic → AdjustPass, OutputMode::LinearWorking	FR-MRG-2's camera-space input, as a compose entry with no operations and the profile uniforms neutral (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 camera-linear	per pixel, full res	GPU, the existing pipeline	It is the pipeline, stopped early
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.

5.1 The tap — S15.3, answered by reading the composer

The fused shader's order, fixed by operation.rs's own tests: warp → as-shot white balance → operations → base curve → camera matrix → store. (Amended 2026-09-27, D19: warp → as-shot white balance → white balance → camera matrix → operations → view transform → store. The tap is unaffected: it has no operations, its caller fills the matrix with the identity, and the composer emits no view transform in OutputMode::CameraLinear.) The store is either the display encode or, in OutputMode::LinearWorking, an unclipped rgba16float of linear sRGB. That mode exists for the detail stage and is selected from the operations, never by a caller flag, so that a shader and the texture bound to it cannot disagree.

The merge wants the values before the curve and matrix (FR-MRG-2), and the composer already makes that a matter of uniforms rather than structure: the white balance, the matrix and the curve's active flag are all in the reserved uniform block, and a fused pass with no operations, as_shot_wb = 1, cam_to_srgb = I and base_curve_last.z = 0 stores exactly camera-linear RGB after the warp. (Since D19 there is no curve flag: the base curve is gone, and the tap composes no view transform, so the white balance and the matrix are the only uniforms it fills neutral.) So the tap is:

• EditGraph::compose_camera_linear() — the LinearWorking tail with an empty operation list and identity framing, paired by name with
• AdjustPass::render_camera_linear() — binds the f16 target, fills the reserved uniforms neutral instead of from the source, returns the texture,
• and a float readback beside the existing 8-bit one.

Nothing in the chain moves. Precision: the tap and every chunk buffer after it should be rgba32float, not f16. A 14-bit sensor has 16 384 steps to white; f16 has 2 048 in the top octave, and a composite that is going to be re-developed deserves the sensor's precision. The cost is 2× on buffers FR-MRG-11 already bounds.

What the DNG carries as a consequence: the first source's Make, Model and UniqueCameraModel — so base_curve::for_body found the 6D's curve, until D19 retired the per-body curves — its ColorMatrix1/2 with illuminants, and its AsShotNeutral. The composite then develops through the same profile as its sources, applied once. The spike's 64 × 48 file (§8) already carries the matrix and neutral; the body name is a string.

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

S15.2, 2026-09-19: XFeat loads under tract. tools/export-xfeat.sh exports the network alone at 768×1024 — thirteen operator types, all standard: Conv, InstanceNormalization, AveragePool, Resize, Slice, Transpose, Reshape, Concat, Add, Relu, Sigmoid, ReduceMean, Unsqueeze — and examples/onnx_probe.rs loads the 2.8 MB file through the app's own ort-over-tract backend with nothing unsupported, in 28 ms, and runs it in ~300 ms on the reference desktop's CPU. The weights ship as models/keypoints/xfeat-1024.onnx, recorded in models/LICENCE.md. On the tablet (S15.4's CPU half, same day): tools/onnx-probe-on-device.sh cross-builds the probe, and the same file runs in ~400 ms per frame on the reference tablet's NEON cores (ROD2-W09, SM8635), with output ranges identical to the desktop's — inside NFR-MRG-1's 1 s per frame with room to spare, and 1.3× the desktop rather than the 2× faces.md §9 measured for its scan. Still to do: a keypoint-level comparison against the PyTorch reference once the Rust decoder exists — the probe proves the graph runs, not that the numbers match.

The outputs are three maps at 1/8 resolution, 96×128 for the export size: 64-channel descriptors, 65-channel keypoint logits (each 8×8 cell's position plus "none"), and a reliability heatmap. The Rust decoder is: softmax over the 65, pixel-shuffle the first 64 to full resolution, 5×5 non-maximum suppression, top-k by reliability, bilinear sampling of the descriptor at each keypoint, L2 normalise. That is detectAndCompute in the reference, minus the network.

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. A linear DNG at the source's native scale: u16 samples on the first source's black-subtracted scale, WhiteLevel = its white minus its black (13 023 for the 6D set: 15 070 − 2 047), BlackLevel = 0. Not rescaled to 65 535 — the sensor had 14 bits and the file says so, and a value the sensor could not have produced is not invented by a multiply. The first source's Make, Model, UniqueCameraModel, ColorMatrix1/2, CalibrationIlluminant1/2, AsShotNeutral and EXIF are carried, so the composite develops through the same profile as its sources. Named from the first source with a -pano suffix, beside it.

Three samples per pixel rather than a CFA: the warp resamples, and there is no sensor grid to mosaic back onto. Nothing else about being a RAW is lost — no white balance, no curve, no matrix, no clip has been applied — and the photographer develops the panorama afterwards as one photograph. One sample is rewritten: a blown one, which is written as the camera value the composite's balance calls grey rather than as the sensor's (1, 1, 1) — §15.

The sources are portrait frames in the 6D set: Orientation is applied before alignment (learned features are not rotation-invariant) and the composite is written upright with Orientation = 1.

Two containers were candidates and S15.1 decided, on 2026-09-19:

• 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.

Linear DNG. examples/linear_dng.rs hand-rolls a 64 × 48 LinearRaw DNG — one IFD, uncompressed 16-bit RGB, DNGVersion, ColorMatrix1, AsShotNeutral, CalibrationIlluminant1 — and rawler 0.7 reads it back: cpp 3, the samples interleaved as written, the matrix parsed into the camera definition, and CameraProfile::extract builds the same profile it would for a camera file. ImageMagick's libraw reads the same bytes. What does not yet work is dr_decode::decode, which accepts the file as CFA and hands the pipeline three times the samples it expects: the cpp == 3 branch is the work, and it is the only decode work.

The composite therefore enters the pipeline as a non-CFA, linear source — from_rgba8's sibling with non_linear = false and the colour matrix carried from the DNG — and is developed as any RAW is. The writer is the example's IFD, grown up: tiled rather than one strip (FR-MRG-11 encodes per chunk), and carrying the first source's EXIF in a sub-IFD as dr-export already does.

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. (Since 2026-09-27 each row has a box: an unticked frame is left out and the rest are solved again from what the first pass measured — §15.)
• 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.

11. Where it stands — 2026-09-19, end of the first day

Built, on branch merge/panorama, in the order §10 gave:

Piece	Where	State
Geometry: keypoints, matching, homography, focal, bundle adjustment, projections	core/dr-pano	Done; 33 tests without a model; the fixture aligns in 4.5 s
XFeat at two shapes under tract	models/keypoints, dr_pano::xfeat	Done; 300 ms/frame desktop, 400 ms tablet
The camera-space tap	OutputMode::CameraLinear, AdjustPass::render_camera_linear	Done, rgba32float, tiles by view rect
Linear DNG writer, streamed	dr_export::write_linear_dng	Done; rawler reads it back
A three-sample RawImage re-entering the pipeline	dr-decode, DemosaicedImage::from_linear_rgb16	Done
Warp, accumulate, resolve, chunk by chunk	dr_gpu::MergePass, merge.wgsl	Done; feathered blend, scalar gain
The job: load, proxies, align, gains, confirm, merge, provenance	dr_ui::merge	Done; examples/merge.rs drives it headless
The page: table, preview, projection, Merge/Stop/Back; the grid's button	merge.slint, merge_ui.rs	Done; DARKROOM_START_MERGE=a.CR2,b.CR2 lands on it
Placement beside the sources through the outbox, rescan	merge_ui.rs	Done, untested against a server

Measured on the fixture (desktop, 12 × 20 MP, Intel adapter): proxies and keypoints 4 s, alignment 4.5–12.6 s (load-sensitive: the matcher is every core), the merge 26 s for a 22 993 × 5 980 composite in twelve bands of 2048 × 512 chunks, 45 s all told, an 825 MB DNG. NFR-MRG-1's 60 s holds on the desktop with room; the tablet's figure is still S15.4's open half.

Open, in the order they matter:

1. Auto-crop (FR-MRG-4). The merge returns a coverage mask per band and the file carries the black border. The largest inscribed rectangle over the coverage, then the DNG's DefaultCropOrigin/DefaultCropSize, so nothing is thrown away and the develop view opens on the picture.
2. Seams and the pyramid (§10 step 5). The feather hides exposure and small misalignment; parallax on the near slope will show as a soft double edge at 1:1.
3. Vignetting in the tap. The lens profile's distortion is applied before the fetch; its vignetting is an operation and is not. Frame edges are darker than their centres by the lens's falloff, and the feather averages them into the overlaps.
4. The tablet: memory (twelve 40 MB sensor buffers on the CPU, one demosaiced frame at a time on the GPU), the figure, and FR-MRG-9's ceiling.
5. Horizon and drag-to-correct (FR-MRG-4, the proposed 4a). The alignment failed on nothing in the fixture; the interaction waits for a set it fails on.
6. derived_from names sources by file name, not content hash: the catalog's content_hash is null for most images most of the time. The hash can join it when the catalog has one.

12. Filling the border instead of cropping it — MI-GAN, read and measured 2026-09-19

Raised after the first merges: the ragged border a cylinder leaves could be filled rather than cropped away. FR-MRG-4 says no boundary fill, on §1.3's "not a pixel editor"; this is the evidence for deciding whether to revise that, not a revision.

The candidate: MI-GAN (Sargsyan et al., ICCV 2023, Picsart AI Research). Image inpainting designed for mobile: ~6 M parameters, plain convolutions — no FFT, no attention — so it quantises to int8 and runs on a phone's DSP, with quality close to LaMa and CoModGAN.

Licence: MIT, code and weights alike (LICENSE and LICENSE-WEIGHTS in the repository, read the same day). The cleanest position of any model in the tree — GPL-compatible, store-compatible, no grant to read around.

Export. The HuggingFace ONNX files are the pipeline — uint8 image and mask in, crop-around-mask, resize and blend inside the graph, every dimension dynamic — and tract refuses them (F6 again). The bare generator exports cleanly from the migan_512_places2.pt state dict at a fixed 1×4×512×512 (export_migan.py in the spike directory; the input is mask − 0.5 and the masked RGB in −1..1, the output RGB in −1..1, the caller composites). After slimming the graph is six operator types: Add, Clip, Conv, LeakyRelu, Mul, Resize. 28 MB.

Under tract on the reference desktop: loads in 53 ms, runs in 7.4 s per 512 × 512 tile, f32. That is the number. The fixture's border is two ragged bands across 22 993 px — roughly ninety 512-px tiles at full resolution — so a CPU-f32 fill is ten minutes on the desktop and longer on the tablet. Three ways to make it viable, none built:

1. Fill at a quarter of the resolution and upsample. Sky and scree tolerate it; twenty-odd tiles, about three minutes on the desktop CPU. A background job with the outbox's patience, not an interactive one.
2. int8 on the tablet's Hexagon through QNN, where the plain-conv design is the point and the whole graph should run in milliseconds. The setup exists from the eye-state work; MI-GAN is a candidate for the same path.
3. A WGSL runtime for those six operators. A project of its own, and the only route that would make it interactive on the desktop.

Whichever, the fill is a proposal under FR-MRG-1's rule — shown, then confirmed — and it would sit beside the crop, not replace it: the crop is free and honest, the fill is invented pixels, and the photographer chooses.

13. The fill, built — 2026-09-19, evening

Built the same day on merge/fill, on the engine (S16) rather than tract, and FR-MRG-4 revised to admit it: the border is cropped or filled, the photographer's choice, the crop the default.

What runs. dr_pano::fill is the engine-independent half: an Inpainter trait (a 512-px tile in, the same tile out) and fill_border, which owns everything the model does not — which tiles, what context, how to blend. dr_pano::migan::MiGan is the trait over the shipped generator under dr_inference_engine with the new Role::Inpainter, so it takes whichever rung the device has. The merge job runs the fill at half the composite's resolution, in a display-ish space (white balance, camera matrix, gamma — invertible, so the result goes back to camera-linear and into the same linear DNG), and the full-resolution merge samples the fill where no frame reached.

What the spike taught, tried in order and kept or dropped.

1. Context across the coverage edge. MI-GAN was trained on holes inside pictures; given a hole at the picture's edge it invents a structure along the open side (white streaks in the sky, on the first try). The known content is therefore mirrored across the coverage edge into the hole and into a 256-px ring, column by column for the top and bottom bands and row by row for the sides; the model interpolates between real and mirrored sky rather than extrapolating into nothing. Replicated rows (the edge row continued flat) streaked the grass; a detrended mix (tone replicated, texture mirrored) smeared; a low-pass extrapolation banded. Mirror stays.

2. Coarse to fine. One pass at the working resolution let the boundary leak in — each 512 tile saw only its own corner of the hole. So a coarse pass at a quarter decides the structure with the whole border in a few tiles, and fine passes in 96-px bands from the real edge outward regenerate texture, each band the only unknown with the previous band on its near side and the upsampled coarse fill on its far side.

3. The seam. A hard cut between real and invented showed as a sharpness step. The known mask is eroded by a 24-px feather (48 at half resolution) and the fill blended in across that margin by distance to the real edge, smoothstep.

4. Partial pixels. The seams were still visible until the cause was found upstream of the fill: the camera-space tap stored black with alpha 1 for a pixel the lens correction pushed off the sensor, and the warp averaged it in — a dark, poorly interpolated fringe along every frame's edge that the fill then continued. OutputMode::CameraLinear now stores alpha 0 for a pixel that is not there and the merge's warp weights by the sampled alpha, so the fringe never enters the composite. The mask erosion before the fill dropped from 16 px to 4.

5. What is still wrong, and why it ships anyway. With the seams gone the content itself is the problem in the deep corners: the model, trained on Places2, puts bright cloud-and-peak shapes into a sky hole and a water-like band under grass — its prior for "top of a picture" and "bottom of a landscape", not anything in the context (the same shapes appear with the mirror capped, uncapped, and on the CPU as on TensorRT). Thin borders are fine; that is most of a hand-held sweep. So the fill ships experimental: opt-in, previewed, its knobs on the page and in the sidecar, and cargo run -p dr-ui --example fill re-runs any merge's dumped input (DR_FILL_DUMP=dir) stage by stage in seconds so the next attempt is made from the picture, not from a seven-minute merge. Candidates for that attempt: a context that is not a mirror at all in deep holes (the coarse pass's own answer, iterated), a sky detector that fills sky by extrapolating the gradient and leaves the model to texture, or a different model.

Measured, the fixture's twelve frames (22 991 × 5 978), 348 tiles at half resolution. 312 s on ONNX Runtime's CPU pool on the reference desktop (≈ 0.8 s a tile). On TensorRT fp16: 100 s, of which 60 ms a tile was the engine hashing the 28 MB model on every acquire (fixed, the hash is taken at open) and 150 ms a tile the GPU itself — throttled: trtexec on the same engine read 23 ms at noon on a cool machine and 152 ms that evening after two hours of builds, nvidia-smi showing SW power cap and thermal slowdown. Cool, the fill is ~10 s. The TensorRT engine compiles once, in 13 minutes, cached under the inference directory.

The runtime is a packaging matter. Arch's onnxruntime-opt-cuda has no TensorRT provider ("not enabled in this build") and its CUDA provider does not load against cuDNN 9, so on this machine the app fell to ORT CPU until the official onnxruntime-linux-x64-gpu_cuda13 tarball (1.30.0, which links the system CUDA 13.4 and TensorRT 10.16) was unpacked and named with DARKROOM_ORT_DIR; /usr/lib/darkroom is searched too, for a package that ships it. §12's point 2 for the tablet is unchanged.

On the page. A Border choice beside the projection — Crop to the picture / Fill the border — with a caption saying what the fill is; the preview re-renders filled when chosen, at preview resolution, so the choice is seen before it is confirmed (FR-MRG-1). Greyed out with the reason when migan-512.onnx is not in the model directory. Under the fill, while it is experimental, its six knobs as sliders — working scale, edge erosion, coarse pass, band width, mirror depth, seam feather — each committing a redraw of the preview. A filled merge's sidecar says border filled with the knobs used, and its default crop is still the inscribed rectangle.

Ships. models/inpaint/migan-512.onnx (LFS, 28 MB, MIT, models/LICENCE.md), installed by the PKGBUILD and unpacked by the APK beside the face and scene models; tools/export-migan.sh regenerates it from the upstream checkpoint.

14. The fill, trained — 2026-09-20

§13.5 named the remaining fault: in a deep corner the stock model puts its Places2 prior — clouds, peaks, a water line — into a hole, because it was trained on holes inside pictures and a panorama's border is a hole with the picture on one side and nothing on the other. Every ring (mirror, replicate, detrend) treated the symptom. The fix is a model that has seen the real thing: MI-GAN's 512 generator fine-tuned on border-shaped voids cut from the user's own photographs, in a separate repository (darkroom-infill, beside this one), so the truth beyond the void is known and the model learns one-sided extrapolation.

What it was trained on. Voids made the way this merge makes them: frames with a yaw, a common pitch and per-frame roll, projected onto the cylinder and rasterised, the canvas their union's bounding box, the void the canvas outside the union — arcs where straight edges bent, cusps where frames meet, the bow-tie wedge at a corner (a third of tiles are cut at a canvas corner). Voids to 256 px deep at a 512 tile. Half the deep tiles train the second pass: a no-grad first pass fills the tile, its nearest band (64–256 px) is marked known, and the remaining void is the example — so the model continues its own output without drift, which is how fill runs deep voids. Data: ~7 400 pictures — the 1024-px proxy tier of the library and ~2 000 raws sampled evenly across every year, developed at half size. Loss: hole-weighted L1, VGG16 perceptual, a hinge PatchGAN. One night on the reference desktop's RTX 3050.

What changed here. FillParams::mirror_depth 0 is now "no ring": the void reaches the tile's edge with nothing beyond, and beyond the band being filled the void stays unknown rather than presented as known coarse fill — the two conditions the model was trained under. Defaults: mirror 0, coarse 1 (the coarse pass seeds nothing the model is allowed to see), band 192. The ring remains on the page for the stock model's sake, at any depth above zero. The model file is a drop-in (models/inpaint/migan-512.onnx, same six operators, same tensors) and the engine loads it unchanged.

Measured, 240 held-out tiles with projection-shaped voids (PSNR in the hole, dB / LPIPS on the composite), stock → shipped (step 3 607): edge 16.9 → 18.5 / 0.121 → 0.136; corner 14.6 → 16.3 / 0.183 → 0.205; interior 18.2 → 19.3 / 0.051 → 0.056. Read both columns: the fine-tune gains ~2 dB on edges and corners because it stops inventing objects, and loses on LPIPS because what it paints in a deep void is smoother than the stock model's confident wrong texture — LPIPS rewards texture, right or wrong. On the fixture's dump at half resolution (the merge's working size) the sky corners are sky, with no structure and a faint tone step at worst; the ground bands carry a fine texture at the right tone, softer than the real scree above them. The stock model's top-left corner on the same dump is a glowing invented structure. The training's own record — what each loss weighting did, and the two runs abandoned (blur under L1 in the hole; a brick pattern under a strong adversarial term against a discriminator that had not learned) — is runs/ in darkroom-infill.

Second model, the same day. The morning's fill was soft in the deep ground bands. The afternoon's run trained the generator against MI-GAN's own pretrained discriminator (non-saturating loss, lazy R1, feature matching), with fresh noise inputs while training and flip/translation augmentation of the discriminator's input — both needed, or the generator settles into a periodic texture the discriminator cannot see. The shipped weights (step 4 750 of runs/border-v6) are level with the stock model on LPIPS (edge 0.125 / corner 0.187 against 0.121 / 0.183) while keeping the PSNR gain (edge 18.0 / corner 15.7 against 16.9 / 14.6). On the fixture the ground bands now carry texture at the right tone; at 1:1 a faint regular hatch is visible in the deepest part.

What is still wrong. The hatch, and any deep textured void the generator must invent. The better answer for those is not generative: seed the void with the picture's own texture in hexagonal cells, let the discriminator rank the candidates, and let the generator heal only the gaps between cells — built and measured in darkroom-infill (infill/hexfill.py), the most convincing scree corner produced so far, and the next thing to port into dr_pano::fill (it needs the discriminator as a second model, ~80 MB fp16). FR-MRG-4's experimental stays.

15. Leaving a frame out, and white clouds — 2026-09-27

A frame is left out from its row, not by starting again. Until now a frame that did not fit ended the job with its name, and the only way on was Back, a smaller selection, and every frame read, demosaiced and searched for keypoints again. Each row of the Frames table on the merge page now has a box, ticked by default. Unticking one leaves the frame out and the rest are solved again at once; ticking it brings it back.

What makes that cheap is a split in dr_pano::align. match_pairs does the matching and the pairwise RANSAC once over every frame — about 5 s for the twelve-frame fixture — and solve takes a subset and uses only the links among the frames in it, about 0.1 s. Solving a subset by re-aligning it from scratch was tried and is wrong: the RANSAC seeds are keyed on frame position, so dropping a frame moved every seed after it, and on the fixture that was enough to lose a marginal link and strand a neighbour of the frame left out. A frame whose only overlap was with one left out is reported unaligned, exactly as it would be had it never been measured with it.

A frame that cannot be placed no longer ends the job either (FR-MRG-5). Its row names it and says why, and Merge stays off until it is unticked — still never a silent drop. The headless example leaves such frames out the same way, and takes --leave-out N to untick frame N once the first alignment is in.

Blown highlights stay white. A clipped photosite reaches the merge as camera (1, 1, 1), which the as-shot balance turns magenta. The alignment preview balanced it with no highlight rule, so every blown cloud was pink on the page. The DNG had a quieter form of the same fault: a frame's gain below one moved a blown sample off the white level, and the feather mixed it into a neighbour's real sky, after which the develop's own highlight desaturation no longer recognised it. The merge shader (merge.wgsl) and the preview (grey_if_blown in dr_ui::merge) now write a blown sample, before the gain, as the camera value the composite's balance maps to grey — the develop pipeline's neutral, fading in from CLIP_ONSET exactly as the develop's does.

A composite this wide is past two limits, both lifted the same day. They were found on a 22 927 × 8966 panorama from Lightroom, and the fixture's own composite (22 993 × 5 980, §11) is past both. rawler's allocation guard, sized in samples but worded in pixels, refuses a three-sample DNG past about 16 700 pixels wide; the copy in third_party/ carries it raised (README). And no texture holds such a frame: a linear DNG past 8192 pixels now opens on a box-reduced copy, a render finer than the copy samples a window of the full resolution, and the export is drawn in tiles (FR-DSP-2's note in requirements.md, ARCH §5.3).