Nvidia introduced a new project called PPISP aimed at improving the level of detail in rendered gsplat scenes. This approach separates camera-specific imaging effects from the underlying scene representation to produce more realistic novel views.

Method

The PPISP pipeline trains a separate model dedicated to synthesising new viewpoints from photographs while conditioning on camera parameters extracted from EXIF. By factoring out exposure and white balance influences, the system isolates scene geometry and appearance parameters from imaging artifacts.

As a result, the model can predict how a scene would look under different capture settings without conflating sensor and lens effects with actual scene detail and texture.

Integration and deployment

Developers report that PPISP has already been integrated into the gsplat renderer and will be added to 3DGRUT in a subsequent update. Integration allows existing workflows to leverage the new conditioning mechanism without changing core scene representations.

Technical highlights

• PPISP uses camera metadata conditioning, such as exposure and white balance, to decouple imaging effects from scene content.
• The architecture trains an independent network for view synthesis, reducing entanglement between sensor artifacts and geometry fidelity.
• Initial integration targets focus on improving photorealism in rendered splat-based point representations and multi-view compositing.

Outlook

By separating camera-induced variations from scene reconstruction, PPISP aims to produce more consistent and realistic novel views across varying capture conditions. The change promises higher fidelity in downstream rendering and compositing tasks without altering existing dataset formats.