PPISP on a real 3-camera capture
NVIDIA just shipped PPISP. We put it through a clean ablation inside our Scene→Sim pipeline — same capture, same 30k iterations, one toggle. Drag to see the difference.
PPISP off PPISP on Drag the handle — or focus it and use ← / → — to wipe between off and on.
What you're looking at
PPISP is NVIDIA's method for the photometric artifacts that wreck radiance-field and Gaussian-splatting reconstructions — the lighting and colour drift that turns a clean multi-camera capture into mush. It slots on top of existing pipelines and works across 3DGS, 3DGUT and NeRFs.
We ran a single controlled ablation: the same capture and the same 30,000 training iterations, changing one thing — PPISP off versus on.
Off → the scene collapses into a hazy, washed-out haze; the sculpture and water dissolve.
On → edges hold, colour stays true, and the reconstruction lands +9.5 dB PSNR higher — a large, plainly visible jump.
Reconstruction methods benchmarked in this line of work:
Why it matters for Forenly
Our Scene→Sim pipeline turns real multi-camera captures into simulation-ready 3D scenes — the digital twins where humanoid robots learn to walk, navigate and manipulate before anything runs on hardware.
In that pipeline, reconstruction fidelity is the training ground. A blurry scene is a broken one: geometry the robot can't trust and textures the policy can't read. So every dB of reconstruction quality is a better place to learn a skill — which is exactly why we test methods like PPISP the moment they land.
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The Skill Layer for humanoid robots
Forenly AI turns real scenes into simulation where humanoids learn their skills — then transfers them onto the machine.
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