introducing physically based rendering

[aman99dex]

Proposal: Introducing Physically Based Rendering (PBR) into Instant-NGP

Hello everyone,

First of all, thank you to the authors and contributors of Instant-NGP for releasing such a fast, elegant, and practical implementation of NeRF and related neural graphics methods. The combination of hash-grid encoding with real-time training makes this repository a strong foundation for experimentation beyond classic radiance-field reconstruction.

This discussion is to explore the possibility of introducing Physically Based Rendering (PBR) concepts into Instant-NGP.

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Motivation

Instant-NGP currently predicts view-dependent radiance directly, which implicitly bakes lighting and material properties into the output. While this works extremely well for novel view synthesis, it limits:

• Relighting under novel illumination
• Material editing (albedo, roughness, metallic)
• Physically meaningful scene understanding
• Generalization across lighting conditions

Recent research directions such as PBR-NeRF, Neural Reflectance Fields, and Relightable NeRFs show that disentangling geometry, material, and lighting enables these capabilities. Given Instant-NGP’s real-time performance and efficient hash-grid encoding, it seems like a strong candidate for exploring real-time or near real-time PBR-aware NeRFs.

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Possible Directions (Open for Discussion)

Below are some high-level ideas that may align with the current architecture:

1. Material Parameter Prediction

Extend the network to predict intrinsic material properties, for example:

• Albedo (RGB)
• Roughness
• Metallic (optional)

These parameters could be used with a physically based BRDF such as GGX or Disney BRDF.

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2. Lighting Representation

Possible lighting parameterizations include:

• Learnable environment maps
• Spherical harmonics–based lighting
• Light probes (global or local)
• Fixed lighting vs jointly optimized lighting

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3. Rendering Equation Integration

Replace direct radiance prediction with a simplified rendering equation:

[
L_o(x, \omega_o) = \int f_r(\omega_i, \omega_o), L_i(\omega_i), (\mathbf{n} \cdot \omega_i), d\omega_i
]

Analytic approximations or Monte Carlo sampling could be used to balance physical correctness and performance.

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4. Losses and Physical Constraints

To address ambiguity and instability, additional constraints could be introduced:

• Energy conservation loss
• Albedo smoothness / spatial priors
• Roughness regularization
• Multi-view photometric consistency

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5. Optional / Experimental Mode

To preserve the current workflow:

• Keep existing Instant-NGP behavior as the default
• Introduce PBR as an optional or experimental mode
• Enable via compile-time or runtime flags

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Challenges (Acknowledged)

Some anticipated challenges include:

• Performance impact on real-time training
• Ill-posed material–lighting decomposition without strong priors
• Increased compute and memory usage
• Unknown or uncontrolled lighting in common datasets

Even so, a simplified or approximate PBR formulation may already enable useful relighting and material-editing capabilities.

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Feedback Requested

I would appreciate feedback on:

• Whether this direction aligns with the project’s scope and long-term goals
• If similar experiments have already been explored
• Feasibility within the current CUDA + hash-grid pipeline
• Whether an experimental prototype or pull request would be welcome

I would be happy to help prototype or experiment if there is interest.

Thank you for your time, and thanks again for maintaining this excellent project.