GSOPs for Houdini adds new features

GSOPs, the Gaussian Splatting plugin developed to work with SideFX's Houdini has seen a constant flow of updates.

GSOPs, the Gaussian Splatting plugin developed to work with SideFX's Houdini has seen a constant flow of updates.

Michael Rubloff

Michael Rubloff

Michael is the Founder and Managing Editor of Radiancefields.com

GSOPs

GSOPs, the Gaussian Splatting plugin developed to work with SideFX’s Houdini has seen a constant flow of updates.

There have been new nodes added including the ability to relight gaussian splat models using image-based lighting techniques. Here’s an example featuring Henry Pearce of Infinite Realities.

Image-Based Lighting (IBL) is a technique in 3D graphics that leverages digital images to simulate the complex interplay of light within a scene, offering unparalleled realism and depth. By using high dynamic range images (HDRIs) to capture real-world environments, IBL ensures that digital objects are lit with the nuanced subtleties of natural light, seamlessly integrating them into their surroundings. This approach not only enhances the visual quality of the render but also streamlines the workflow, allowing artists to achieve photorealistic results with greater efficiency and flexibility.

In addition to the IBL node, you now have the ability to transform a gaussian splat model, which is especially useful for scaling up or down a capture.

There are also a few new example scenes from GSOPs for relighting, an IBL node, and animation/batch export support.

Rhodes has also indicated a personal wish list for GSOPs to support including:

  • Viewport Renderer (WIP--likely using glsl).

  • Karma/LOPS render support (this has been requested by a few people already).

  • Segment Any 3D GAussians (SAGA) or similar segmentation integration.

  • Custom python viewer states + “magic wand” functionality (e.g., spatial-aware selection according to attribute similarity).

  • Splat lighting toolkit.

  • PDG/TOPS support for batching and automation (probably to generate synthetic training data with gaussian_splats_generate_training_data).

  • Improved camera coverage generator for gaussian_splats_generate_training_data. My first-pass (object-centric) implementation is somewhat naive. It would also be great to have a camera coverage solver for environments.

  • Icons (I borrowed the best-match icons I could find from SideFX, but custom icons would be great).

  • Promotional material (graphic design or other content you make using GSOPs).

  • I/O for compressed splats. There’s no standardized compression format for 3DGS yet. However, PlayCanvas’ SuperSplat compressed.ply format provides a good balance of simplicity and compression rates. The discussion around standardizing compressed 3DGS has stalled, but maybe you can help breathe life into it!

Also, congratulations to GSOPs developer David Rhodes for starting a new position as a Senior Technical Director within the Innovation Lab at Pixomondo! There are still more updates to come from Rhodes and users are encouraged to build on top of the MIT Licensed platform.


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