How many cameras, and where, to film a person from every angle?

What it found

A 2-D geometric model of N cameras on a ring or a 90° front arc 3 m from a subject, scored on angle coverage and triangulation error. Cameras buy coverage, angle buys depth accuracy: a 30° gap between neighbours needs 13 cameras (15° needs 25), while centre depth error barely moves with count (0.52 mm for 3 on a ring, 0.23 mm for 16) but falls 11× as a pair’s convergence angle goes from 5° to 90° (7.3 mm to 0.64 mm). Four cameras on a front arc are within 11% of four on a ring. Idealised lower bounds with assumed noise (0.5 px) and no occlusion; novel-view quality is not modelled.

A figure from its own output

Paper, code and results

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

More in Autonomous multi-angle capture

• How far apart can cameras be before a reel editor can no longer synthesise the angle between them?How far apart can cameras be before a reel editor can no longer synthesise the angle between them? With exact depth a two-view warp beats cutting to the nearest camera only up to about 30° (24.8 vs 23.6 dB); beyond that holes dominate, and a bystander pulls the limit in to about 15° (ray-traced toy scene; ordering, not decibels).
• 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.
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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.