Epipolar template matching for stereo correspondence, exported from VIAME as a single ONNX graph. Given a set of points in the left image of a calibrated stereo pair, it returns the matching points in the right image, plus match scores.

This is VIAME's stereo measurement method 1 (epipolar_template_matching): for each source point it walks the epipolar curve implied by the calibration, sampling candidate depths, and picks the best NCC (TM_CCOEFF_NORMED) template match. Pair it with two-view triangulation to measure real-world lengths — the usual application is measuring fish in stereo camera rigs.

There are no learned weights. The graph is pure geometry plus normalized cross-correlation, which is why it is 89 KB. Nothing here was trained, and there is no training data or evaluation set.

Two parameters are frozen into the graph at export time and are not runtime inputs:

These match configs/pipelines/interactive_stereo_template.conf in VIAME, which is what the DIVE desktop interactive stereo service loads. Note they are not the export script's own defaults (25 / 5000) — re-exporting without explicit flags produces a model that loads identically but matches differently. To reproduce this exact file:

All float32. The world frame is the left camera, so the left camera is normally R_left = I, t_left = 0, and the right camera carries the rig's relative pose. Matrices are row-major.

Grayscale must use BT.601 luma (0.299 R + 0.587 G + 0.114 B) to match the OpenCV BGR2GRAY the reference implementation uses.

The graph deliberately does not apply a score threshold — it returns the best match unconditionally, so the host decides what to accept. VIAME's two reference hosts disagree, and both are defensible:

The uniqueness test, where used, rejects a match when second_score / best_score > ratio — i.e. the winner was not clearly better than an unrelated candidate elsewhere on the curve. Useful on repetitive texture.

The graph takes a depth range, but disparity is usually the more natural way to think about it. Convert with:

min_depth = fx * baseline / max_disparity
max_depth = fx * baseline / min_disparity

where fx = K_left[0][0] and baseline = ||t_right||.

A disparity range of 2–300 px matches the DIVE desktop interactive stereo config, but this is scene-dependent: VIAME's batch measurement pipelines ship 7–724 for other rigs. Too wide invites false matches; too narrow misses the target entirely. Calibrate it to how far the same object actually shifts between your two cameras.

The graph is small and CPU-only, so it runs comfortably in a browser via the WASM execution provider — this is how DIVE warps a detection from one camera to the other with no backend. A complete client-side implementation lives in client/dive-common/use/stereo/ (see Kitware/dive#1709), covering calibration parsing, the search-range conversion above, and two-view triangulation.

This is a bespoke geometry graph, not a transformer. It has no config.json, no tokenizer or image processor, and no architecture in the transformers.js registry, so pipeline() / AutoModel will not load it. Use onnxruntime (or onnxruntime-web) directly.

The graph always matches left → right. To warp a point annotated on the right camera, invert the rig instead of swapping the inputs: the new world frame is the old right camera, so R' = Rᵀ and T' = -Rᵀ·T, with the intrinsics and distortion swapped between sides.

Validated against the VIAME C++ / Python reference implementation to roughly a quarter pixel. Note the exporter's own verification reports a sub-pixel right_points difference between eager PyTorch and onnxruntime (about 0.04 px at template_size=13) caused by ties between equally-scoring epipolar candidates; scores match to ~1e-8.

CC-BY-4.0. Produced by plugins/onnx/export_stereo_mapping.py in VIAME, whose core infrastructure is BSD-3-Clause.