Orange-lined triggerfish · fish2

Balistapus undulatus · Balistidae · 100% · high reconstructed model: good
Reviewer: translucent dorsal/anal fins and yellow caudal fan; tracks well even head-on

Where it comes from

Original video (longest visit, 48:01-48:14), the selected fish boxed
Tracking: real frame + crop + 3D render at the estimated pose, species overlay

3D and 4D model

loading 3D gaussians…
Interactive canonical 3D gaussians (drag to orbit, scroll to zoom). This is the rest pose shared by every frame; the 4D part is the per-frame bending / tail motion shown on the right.
4D: canonical model replaying the learned body bending and tail beat
3D turntable of the canonical model

Previous version (v1) vs v2

v2 = the same 30 fps data and trainer as v1 (gs_final, 10000 iterations) with 12000 iterations and a gaussian cap of 100,000 — the only change of the quality experiment that passed its review. Other changes (frame selection, water and motion-blur models, white balance, lossless crops) did not.
v1 (previous version): gs_final, 10000 iterations
v2: gs_v2, 12000 iterations

The two turntables play in sync (same orbit and timing).

metricv1 gs_finalv2 gs_v2change
iterations1000012000
gaussians26,90829,482x1.10
training time (optimisation)62 min80 min
held-out frames (within 0.3 s of a training frame)16 (16)16 (16)
LPIPS on the fish (lower = closer to the photo)0.16940.1672-0.0022
PSNR full crop (dB)24.1424.16+0.02
PSNR on the fish mask (dB)21.3121.32+0.01
sharpness ratio render / photo, grain-robust (1 = as sharp as the photo)0.3210.347x1.08
sharpness ratio, raw0.1190.154x1.29
sharp match (photo detail reproduced in place)0.2790.298+0.019
IoU with the SAM mask0.8970.899+0.002

Verdict: same (within noise). LPIPS -0.0022, grain-robust sharpness x1.08 (sharper). Differences below 0.0034 LPIPS or x1.12 sharpness are within the run-to-run noise of training.

Held-out frames: fixed per fish (work/quality_exp/<fish>/heldout.json), evaluated identically for both models (test-time fit of pose, colour and bending with the gaussians frozen). Both models were trained on all frames of the same data set, so held-out frames near a training frame favour both equally; this is a like-for-like comparison of the two recipes, not a generalisation test.

Poster

poster

Species

Orange-lined triggerfish Balistapus undulatus Balistidae
100.0%
Emperor angelfish Pomacanthus imperator Pomacanthidae
0.0%
Titan triggerfish Balistoides viridescens Balistidae
0.0%
Bluethroat triggerfish Sufflamen albicaudatum Balistidae
0.0%
Yellow-spotted triggerfish Pseudobalistes fuscus Balistidae
0.0%

Family: Balistidae 100%, Serranidae 0%, Pseudochromidae 0% · agreement across crops 100% · 64 crops · bioclip-2.5 (hf-hub:imageomics/bioclip-2.5-vith14, open_clip, zero-shot)

Crops used for the species vote

Data and reconstruction

window48:00 – 58:00
visits5: 48:02-48:12, 48:23-48:29, 49:41-49:47, 51:16-51:20, 52:58-53:09
frames (10 fps) detected / kept389 / 359
valid poses (10 fps)312
frames with background plate316
median size241.0 px
30 fps frames / valid poses1146 / 881
method4D GS, fins + bending + tail (30 fps)
model shownv2 (gs_v2, quality experiment rollout)
iterations12000
gaussians29482
training time80 min
trainer validation PSNR full / masked25.5 / 20.4 dB
trainer validation IoU vs SAM mask0.890
near-fish band PSNR (fins)25.9 dB
production statusdone; gs_final = copy of fish2_hf/gs_fins_4d (10000 iters, not retrained)
visual note (curation)Balistapus undulatus (orange-lined triggerfish), likely

Diagnostics

Tracklet contact sheet (selection)
Held-out frames: real crop vs 4D render (composited over the plate)
Held-out frames: render on black
Estimated pose: fitted ellipsoid + axes on crops
Frame cleaning: identity / occlusion over time
Frames down-weighted / dropped by the robust trainer

Downloads

point_cloud.ply (canonical 3D gaussians) · poses_refined.json · model.pt (incl. per-frame 4D params) · species.json · previous version v1: gs_final point_cloud.ply

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