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NeILF: Neural Incident Light Field for Physically-based Material Estimation
Yao Yao, Jingyang Zhang, Jingbo Liu, Yihang Qu, Tian Fang, David McKinnon, Yanghai Tsin, Long Quan
TL;DR
Material and lighting estimation from multi-view images is difficult because complex BRDFs and scene illumination are jointly ambiguous. The paper introduces NeILF, a differentiable rendering framework using neural incident lighting and neural BRDFs with regularization. Across synthetic and real-world datasets, it significantly outperforms prior methods in novel-view rendering quality, while requiring known scene geometry and substantial computation.
Problem
Material and lighting must be recovered jointly from images despite ambiguity between complex BRDFs and high-dimensional scene illumination.
Method
NeILF uses a 5D neural incident light field and MLP-based BRDF within a differentiable rendering framework, with bilateral smoothness and Lambertian regularization.
Results
The method significantly outperforms previous methods in novel-view rendering quality across synthetic, DTU MVS, and BlendedMVS datasets.
Takeaways & Limitations
NeILF can estimate material properties for static scenes with complex lighting and geometry while jointly modeling material and illumination through physically based rendering.
Takeaways & Limitations
The method assumes known scene geometry and requires around 1.5 hours to estimate a scene's BRDF in the current implementation.
Abstract
from arXiv · showhide
We present a differentiable rendering framework for material and lighting estimation from multi-view images and a reconstructed geometry. In the framework, we represent scene lightings as the Neural Incident Light Field (NeILF) and material properties as the surface BRDF modelled by multi-layer perceptrons. Compared with recent approaches that approximate scene lightings as the 2D environment map, NeILF is a fully 5D light field that is capable of modelling illuminations of any static scenes. In addition, occlusions and indirect lights can be handled naturally by the NeILF representation without requiring multiple bounces of ray tracing, making it possible to estimate material properties even for scenes with complex lightings and geometries. We also propose a smoothness regularization and a Lambertian assumption to reduce the material-lighting ambiguity during the optimization. Our method strictly follows the physically-based rendering equation, and jointly optimizes material and lighting through the differentiable rendering process. We have intensively evaluated the proposed method on our in-house synthetic dataset, the DTU MVS dataset, and real-world BlendedMVS scenes. Our method is able to outperform previous methods by a significant margin in terms of novel view rendering quality, setting a new state-of-the-art for image-based material and lighting estimation.
1 Introduction
Material estimation from sparse images is difficult because complex BRDFs and high-dimensional lighting create ambiguity, while prior simplifications limit practical use. NeILF addresses this with a differentiable framework that models spatially varying incident illumination and jointly estimates lighting and materials.
- Motivation: Sparse-image material estimation jointly optimizes spatially varying BRDFs and scene lighting, but their complexity makes the problem difficult.Previous methods simplify material or lighting models to reduce optimization complexity.
- Motivation: Prior approaches often require special capture devices, controlled lighting, or simplified illumination models, limiting practical real-world applications.Examples include co-located flash lights, turntables, additional sensors, and environment maps.
- Approach: NeILF represents scene lighting as a neural incident light field that models direct and indirect illumination, occlusions, and spatially varying effects in static scenes.The representation uses incident location and direction to model illumination beyond a 2D environment map.
- Approach: The framework uses differentiable rendering to jointly optimize a neural incident light field and a neural BRDF representation.The BRDF uses a simplified Disney model, while both lighting and material properties are represented with MLPs.
- Results: The method significantly outperforms previous state-of-the-art methods in novel-view rendering accuracy across several datasets.The paper evaluates the framework on synthetic and real-world material and lighting estimation settings.
- Regularization: Bilateral smoothness and a Lambertian assumption constrain roughness and metallic values to reduce material-lighting ambiguity during optimization.These regularizers address ambiguous decompositions in which lighting and reflective BRDF properties can compensate for each other.
3 Method
The method models spatially varying incident illumination with NeILF and surface materials with a simplified Disney BRDF, then jointly estimates both through differentiable rendering. Bilateral smoothness and a Lambertian assumption regularize the material-lighting ambiguity.
- Neural Incident Light Field: NeILF models incoming light as a function of 3D position and incident direction, capturing direct and indirect illumination plus occlusions in static scenes.Unlike an environment map, this representation models spatially varying illumination for complex geometries and lighting.
- Simplified Disney BRDF: The material model uses MLP-predicted base color, roughness, and metallic parameters within a simplified Disney BRDF.The diffuse and specular terms are parameterized by these surface properties and the roughness-dependent microfacet model.
- Material-Lighting Ambiguity: Joint material-lighting optimization is ambiguous because different BRDF and incident-light combinations can reproduce the observed images.A degenerate solution can assign reflective BRDFs and adjust incident lights to fit the inputs, even achieving a perfect image fit theoretically.
- Regularizations: Bilateral smoothness regularizes roughness and metallic by discouraging rapid spatial changes while using image gradients to guide smoothing.The cost weights material gradients by an exponential function of the input-image gradient, with gradients computed over sampled 3D points.
- Regularizations: The Lambertian regularizer favors high roughness and low metallic when view-dependent lighting is absent.The corresponding cost penalizes deviation of roughness from 1 and metallic from 0.
- Differentiable Rendering Loss: The final training objective combines image reconstruction with smoothness and Lambertian regularization losses.The weights are set empirically to w_s = 10^-4 and w_l = 10^-3 in the reported experiments.
4 Implementations
The implementation discretizes incident illumination with fixed Fibonacci samples, renders radiance using the resulting solid-angle approximation, and learns HDR-to-LDR conversion for real-world images. Network architectures, sampling counts, optimization settings, and compute requirements are specified for training and evaluation.
- Sphere Sampling: The rendering equation is discretized over sampled incident lights, with each sample weighted by its corresponding solid angle.The sampled set S_L contains incident directions for a surface point, and Monte Carlo methods normally approximate solid angle using sample probabilities.
- Sphere Sampling: Random sampling produced erroneous BRDF results, so the method uses fixed Fibonacci sampling over the hemisphere with A(ω_i) = 2π/|S_L|.This fixed weighting supports accurate gradient transmission during differentiable rendering.
- HDR-to-LDR Mapping: Real-world LDR images are handled by learning an HDR-to-LDR mapping, while exposure and white balance are absorbed into the incident light.Only gamma correction is explicitly modeled in the renderer.
- Network Architecture: The BRDF network is an 8-layer Siren with 512 features and a middle skip connection, while the NeILF network uses 128 features and an exponential output activation.The exponential activation guarantees non-negative and unbounded light intensities.
- Training Configuration: Training uses 128 incident lights and 16,000 randomly sampled pixels per iteration for 15,000 Adam iterations, while testing uses 256 incident lights.The reported setup takes about 1.5 hours on a Tesla V100 and consumes around 30 GB of VRAM.
5 Experiments
Experiments evaluate NeILF against environment-map and related baselines on synthetic and real-world scenes. NeILF consistently improves novel-view rendering and handles complex, mixed lighting more effectively, while regularization and sampling choices affect reconstruction quality.
- Synthetic benchmark: The study compares NeILF with PhySG*, SG-Env, Pix-Env, and Ne-Env using synthetic scenes with environment-map and mixed lighting conditions.The synthetic dataset includes three object configurations, six lighting conditions, 96 images per scene, and held-out evaluation views.
- Synthetic benchmark: NeILF consistently achieves the best novel-view rendering quality and produces stronger BRDF estimates when multiple objects or mixed light sources are present.SG-Env can be comparable for single objects under environment-map lighting but degrades substantially with multiple objects or mixed sources.
- Real-world scenes: Qualitative comparisons show that NeILF removes highlights from base color and produces plausible roughness and metallic maps on DTU and BlendedMVS scenes.The synthetic comparison also reports noisy SG-ENV BRDF outputs in occluded regions and wrongly recovered highlights under mixed lighting.
- Real-world scenes: On DTU and BlendedMVS, NeILF produces the best rendering PSNR and the most visually pleasing BRDF estimates across the selected scenes.The real-world experiments use reconstructed geometry rather than ground-truth geometry and evaluate held-out test images.
- Ablation study: Higher ray-sample counts improve reconstruction, while random sampling worsens results, indicating that precise discretization of the rendering equation matters.The default uses SL = 128 to balance quality with VRAM and runtime consumption.
- Ablation study: Bilateral smoothness substantially improves roughness and metallic estimates for real-world scenes, but the two heuristics have limited quantitative effect on synthetic scenes.The authors retain both regularizations by default while recommending selective use according to input-scene characteristics.
6 Discussions
The discussion relates NeILF to NeRF through shared neural representations while distinguishing their physical rendering formulations. It also identifies training speed and the requirement for precomputed geometry as important limitations.
- Relation to NeRF: NeILF and NeRF both map a 3D position and viewing direction to an RGB value, but NeILF represents incident lighting rather than radiance.Both representations have comparable input-output complexity despite modeling physically different quantities.
- Relation to NeRF: NeILF recovers spatially varying BRDF properties, whereas NeRF optimizes a density field; their learned properties differ in dimensionality and physical meaning.The BRDF uses a 5D parameter vector, while NeRF’s density is a 1D scalar.
- Rendering formulation: NeILF integrates incident light over the hemisphere using physically based rendering, whereas NeRF accumulates color along viewing rays through volume rendering.Rendering one pixel requires one BRDF evaluation and multiple incident-light evaluations in NeILF.
- Ambiguity and regularization: Bilateral smoothness improves roughness and metallic quality for real-world scenes, helping address material-lighting ambiguity.The discussion connects this effect to the broader role of regularization in disentangling learned scene properties.
- Limitations: Training is time-consuming because rendering one pixel requires multiple light-MLP samples; the reported scene-level BRDF estimation time is around 1.5 hours.The authors suggest explicit octrees, spherical harmonics, or neural hashing as future acceleration directions.
- Limitations: The current method assumes scene geometry is given in advance, although reconstructed meshes were sufficient for the reported DTU and BlendedMVS scenes.Future directions include joint geometry optimization through displacement or normal maps and differentiable surface refinement.
7 Conclusions
The paper presents NeILF as a differentiable framework for jointly estimating materials and lighting from multi-view images and reconstructed geometry. Across synthetic and real-world datasets, it reports substantially better novel-view rendering than environment-map methods.
- Conclusion: NeILF models static-scene illumination with a neural incident light field and jointly optimizes it with material properties through physically based differentiable rendering.The representation accommodates complex lighting and geometry, including direct and indirect illumination and occlusions.
- Conclusion: Across synthetic, DTU MVS, and BlendedMVS datasets, NeILF significantly outperforms previous environment-map methods in novel-view rendering quality.The paper presents this result as a state-of-the-art outcome for image-based material and lighting estimation.
Supplementary Material for NeILF
The supplementary material provides implementation details and additional results, including relighting results in a supplementary video.
- Supplementary material: The supplementary material contains implementation details and additional results for the proposed method.Relighting results are directed to the supplementary video.
1 Implementation Details
The proposed network architecture is presented as simple and effective, with detailed BRDF component implementations based on spherical-Gaussian and GGX approximations.
- The proposed network architecture is described as simple, effective, and readily re-implementable or extendable.
- The normal distribution function D is approximated using a spherical-Gaussian function.
- The Fresnel term F is specified with a material-dependent base reflectance F_0.
- The geometry term G is approximated using the GGX function.
2 Synthetic Dataset
The synthetic dataset combines controlled camera trajectories with environment, point, and area lighting. NeILF recovers environment maps and explains spatially distinct mixed-light sources consistently across backgrounds.
- The synthetic dataset uses 96 outside-look-in camera positions and mixed lighting with two point lights, two area lights, and one environment map.
- The evaluation selects two surface points on a helmet and cube to visualize estimated incident lights under six lighting conditions.
- NeILF successfully recovers the corresponding environment maps at both selected points for environment-map lighting.
- For mixed lighting, the estimates explain background maps, point lights, and area lights, while preserving consistent point and area lights across backgrounds.
3 DTU and BlendedMVS Datasets
BRDF estimation results are shown for both DTU and BlendedMVS scenes, with outputs organized into base color, roughness, and metallic components.
- BRDF estimation results are provided for all DTU scenes.
- BRDF estimation results are also provided for all BlendedMVS scenes.
- The displayed BRDF outputs are organized as input image, base color, roughness, and metallic.
4 Relighting
The relighting pipeline converts estimated BRDF values into UV texture maps that can be used directly in rendering pipelines.
- Given mesh geometry, Iso-charts parameterizes the surface as a two-dimensional UV map.
- The BRDF MLP evaluates BRDF values for each UV-map pixel from its corresponding surface point.
- The resulting BRDF texture maps can be directly used in rendering pipelines for relighting.