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name: neural-rendering-3d description: "Routes Papers-in-100-Lines NeRF, implicit representation, 3D Gaussian splatting, camera, ray, and neural rendering tasks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Neural Rendering and 3D

Use this sub-skill for NeRF-family renderers, Fourier features, SIREN/MFN, PlenOctrees, Plenoxels, K-Planes, Light Field Networks, 3D Gaussian Splatting, Speedy-Splat, Spherical Voronoi, Splatter Image, and single-view 3D reconstruction entries in Papers-in-100-Lines.

Read these bundled files

• Rendering guide maps the rendering families, key functions, camera/data assumptions, adaptation knobs, and validation signals.
• Troubleshooting covers missing ray datasets, trained Gaussian tensors, camera metadata, CUDA hardcodes, projection errors, memory pressure, and output frame directories.
• Implementation index lists all neural-rendering entries and their evidence labels.
• Dependency and backend guide explains per-entry torch/CUDA version conflicts.
• estimate_render_memory.py estimates rough NeRF and splatting tensor memory before a run.

Trigger routes

• NeRF or volume rendering: ray origins/directions, positional encoding, density/color networks, accumulated transmittance, render_rays, novel view output, few-shot/regularized variants.
• Implicit image or coordinate networks: Fourier feature inverse rendering, SIREN, multiplicative filter networks, Deep Image Prior-like reconstruction when it is framed as a coordinate/image representation.
• 3D Gaussian or splatting: spherical harmonics, covariance construction, projection, tiling, alpha compositing, trained Gaussian assets, camera trajectories.
• Single-view 3D reconstruction: Splatter Image and pixel-to-gaussian workflows, UNet encoders, camera intrinsics, source/target image pairs.
• Memory or backend triage: estimate rays/bins/gaussians before launching a full CUDA rendering script.

Safe workflow

1. Query the catalog if the paper is not explicit:

   python ../../scripts/query_implementation_index.py --group neural-rendering-3d --query "gaussian splatting"
   

2. Read Rendering guide for the selected family and identify the minimal inputs: rays and pixels, camera matrices, trained fields/gaussians, image pairs, or synthetic coordinates.

3. Estimate size before execution:

   python scripts/estimate_render_memory.py --mode nerf --height 400 --width 400 --nb-bins 192
   python scripts/estimate_render_memory.py --mode 3dgs --height 800 --width 800 --gaussians 100000
   

4. For quick debugging, shrink image size, ray count, bins, gaussian count, and chunk size. Replace hard-coded CUDA only when the requested result is a CPU shape check, not a real rendering benchmark.

5. Treat missing data/weights/camera files as stop conditions requiring user input or approved download, not as reasons to fabricate results.

Boundaries

Route GANs, VAEs, flows, diffusion, DreamBooth, and Stable Diffusion to generative-models. Route optimizers, activations, meta-learning, hypergradients, Deep Image Prior as optimization, and Atari RL to optimization-meta-rl. Use paper-catalog-and-execution for initial lookup and dependency planning.