How far apart can cameras be before a reel editor can no longer synthesise the angle between them?

What it found

A ray-traced toy scene, two ring cameras, a depth-based warp to the midpoint, scored against the ray-traced truth. With exact depth, synthesis beats cutting to the nearest camera only up to a gap of about 30° (24.8 against 23.6 dB); at 45° it is worse (19.3 against 23.1 dB) because 8% of the target is a hole neither camera saw, though pixels it does fill stay near 29–30 dB. A bystander pulls the crossover in to about 15°, and depth error on the sources costs about 1.4 dB per percent at first (27.5 dB exact, 22.0 dB at 10%). Toy scene with smooth textures and oracle depth: read the ordering, not the decibels.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

More in Autonomous multi-angle capture

• Is the depth a camera rig can actually measure good enough for the in-between angle?Is the depth a camera rig can actually measure good enough for the in-between angle? Block-matching stereo on rendered pairs feeds the two-view warp: mean depth error 14.9% at 3 cm baseline to 2.7% at 50 cm, coverage peaks at 91%, and the result never beats cutting to the nearest camera (22.3 vs 24.8 dB at a 15° gap; exact depth 27.3 dB).
• Can the holes and correlated errors of measured stereo depth be repaired so the in-between view beats cutting to the nearest camera?Can the holes and correlated errors of measured stereo depth be repaired so in-between synthesis beats cutting to the nearest camera? A hole filler and a no-ground-truth disagreement gate that falls back to the raw nearest camera lift a 15° gap from 22.3 dB to 29.2 dB against 24.8 dB for cutting (held-out seeds 29.2); with a bystander the gate loses its edge (26.1 vs 26.3 for plain filling). Toy scene.
• Which platform can follow a travel creator through a crowd and keep the shot?Which platform can follow a creator through a crowd and keep the shot? A drone that ignores pedestrians frames 99.9% of the time but falls to 39% at 0.3 people/m² once it avoids them; a wheeled robot is stopped by step lines 42–47% of the walk, and a flat-floor one that ignores people passes within 0.5 m ~500 times an hour. 2-D simulation, assumed platform figures.
• How much of a multi-camera shoot can an automatic edit recover, and what stops it?How much of a multi-camera shoot can an automatic edit recover? At detector noise 0.3 a fixed 3 s rotation shows mean true quality 0.546, a cut-cost (Viterbi) edit 0.802 vs 0.815 on true scores, and best-score-every-frame 0.723 at 345 cuts a minute; clock sync fails first on short clips (5 s at noise 0.25: 25% over 40 ms). Synthetic quality model, no real footage.
• When does automatic blurring miss a bystander's face, and which filming platform is conspicuous?When does automatic blurring miss a bystander's face? On an assumed size-recall model a face detector leaves 5% of identifiable faces unblurred per frame, blurring the head of any detected body under 0.1%, and independent per-frame misses show a face in some frame of a 5 s shot 100% of the time; an offline whole-track blur leaves 0 to 5%. No real footage or detector.
• Does recording only when something happens save a travel creator's battery, and what does it cost in missed moments?Does recording only on a trigger save battery, and what does it miss? With 6 thirty-second moments an hour, standby with a 10 s pre-roll uses 19.4 Wh of an 80 Wh continuous day (76% less) and captures 90% of interesting seconds; deep sleep uses 7.7 Wh but captures 69%. Misses and wake latency, not the battery, set coverage. Assumed device figures, synthetic day.

All 34 Autonomous multi-angle capture projects →

Other research topics

Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.