typed op• Data kinds: counts → feature
• Call: fullseye.apply(img, "tb_dtof_depth", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_dtof_depth: knob a sweep (docs site)
*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
Stages (the ops that come before → this op, left to right):
▸ tb_dtof_depth: stages (docs site)
Distance from a photon arrival-time histogram: `d = c*t/2`.
Direct time-of-flight. The light travels to the target and back, so the
one-way distance is half the round-trip time times the speed of light.
*bin_ps* is the width of one time bin (a 100 ps bin is 1.50 cm of depth).
Four estimators, from crudest to sharpest:
• `"peak"` — the centre of the fullest bin. Quantised to the bin grid;
the error is uniform in `+-half a bin (+-0.75 cm` at 100 ps).
• `"centroid"` — the first moment of the whole histogram. Exact for a
symmetric pulse *with no background*, and badly biased toward the middle
of the window with one — pass `subtract_background=True`.
• `"parabolic"` — a parabola through the peak bin and its two neighbours.
Sub-bin, cheap, and biased for a Gaussian pulse.
• `"gaussian"` — the same parabola fitted to the log of those three
samples, which is the exact vertex for a Gaussian pulse.
Measured on a noiseless simulated return at 2.4371 m (256 bins x 100 ps,
500 ps IRF), absolute error: `peak 1.29 mm, centroid` 4.4e-16 m,
`parabolic 0.067 mm, gaussian` 9.4e-9 m — three orders of magnitude
between the crudest and the sharpest.
With Poisson noise (200 signal + 200 ambient photons, seed 0) the same
four give 13.7 mm, 146.5 mm (with `subtract_background=True`), 8.5 mm and
8.0 mm. Two honest readings of that: once shot noise dominates the sub-bin
estimators buy about 1.6x, not three orders of magnitude, and the centroid
collapses because a median-subtracted ambient floor still leaves noise
across the whole window that drags the first moment toward the centre. Use
`"gaussian" or "parabolic" on noisy data; use "centroid"` only when
the background is genuinely gone.
*offset_ps* is a system delay to remove: ``t_flight = t_measured -
offset_ps``, so a positive offset makes the answer *closer*. Returns the
distance in metres as a float.
Raises `ValueError`: negative, non-finite, non-1-D or all-zero *hist*,
a non-positive *bin_ps*, an unknown *mode*, a non-finite *offset_ps*, a
flat histogram in a peak-based mode (`argmax` would silently pick bin 0
and report the first bin's depth), a peak in the first or last bin with a
sub-bin *mode* (there is no neighbour to fit to — use `"peak"`), a
degenerate three-sample fit, and — instead of returning a negative distance —
an *offset_ps* larger than the measured arrival time.
Typed bridge of the photon op `dtof_depth into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. a drives bin_ps (default 100); b` is unused.
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
img_to_counts 0.50 0.50 tb_dtof_depth 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op dtof_depth. This bridge op is the same implementation adapted to the fn(v, a, b) convention, so the behaviour carries over unchanged (only the call form differs).
• photon_timeresolved — py -3.11 examples/photon_timeresolved.py
• poc_dtof_ranging — py -3.11 examples/poc_dtof_ranging.py
feature as input)typed)tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal · tb_voxel_grid_downsample
*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.