Neural Exposure Fields (NExF)

• Neural Exposure Fields (NExF) are neural scene representations that integrate a dedicated exposure field at each 3D point for consistent appearance under diverse lighting.
• NExF jointly optimizes radiance and exposure using multilayer perceptrons, reducing artifacts like overexposure and underexposure in HDR conditions.
• NExF achieves faster training and higher reconstruction quality, broadening applications in augmented reality, photorealistic rendering, and architectural visualization.

Neural Exposure Fields (NExF) are neural scene representations distinguished by their capacity to model and optimize spatially-varying exposure at each 3D point in a scene. Unlike conventional approaches that treat exposure as a per-image or per-pixel parameter, NExF learns an explicit exposure field jointly with the neural scene representation, enabling consistent appearance and accurate view synthesis under challenging real-world conditions such as strong high dynamic range (HDR) variations, indoor–outdoor transitions, and scenes with highly varied lighting.

1. Conceptual Foundations and Motivation

NExF generalizes core principles of neural radiance fields (NeRF) by integrating exposure modeling at the field (3D spatial) level, rather than delegating exposure adjustment to image-based post-processing or relying on multi-exposure capture (Niemeyer et al., 9 Oct 2025). This shift addresses inconsistencies in rendered appearance introduced by scene regions with drastically differing illuminations, e.g., environments with simultaneously visible interior and exterior windows. The model's ability to predict optimal exposure per 3D point enables robust 3D-consistent view synthesis, mitigating artifacts such as overexposure or underexposure that occur when using per-image exposure settings.

2. Technical Formulation

The NExF architecture augments a radiance field with a dedicated neural exposure field, both parameterized as multilayer perceptrons (MLPs). Formally, for any 3D point $x \in \mathbb{R}^3$ and viewing direction $d \in \mathbb{S}^2$, the radiance field $f_\theta$ takes latent exposure conditioning via:

$$f_\theta(x, d, \Delta t(r)) = f_\theta^\text{view}\left(f_\theta^\text{pos}(x) + \ln \Delta t(r), d\right)$$

where $\Delta t(r)$ is the (logarithmically transformed) exposure associated with ray $r$, and $f_\theta^\text{pos}$ is the intermediate spatial embedding. The exposure field $e_\phi$ is a separate MLP:

$e_\phi: \mathbb{R}^3 \rightarrow \mathbb{R}$

predicting the optimal exposure value $\hat{\Delta t}(x)$ at every spatial location. During training, volume rendering integrates density and exposure-conditioned color along each ray:

$$c_\text{pixel} = \sum_{j=1}^{n_s} \tau_j \alpha_j c_j$$

with standard transmittance and opacity calculations. Exposure conditioning is injected at a latent (bottleneck) level rather than post hoc to the output color, yielding greater robustness across lighting levels (Niemeyer et al., 9 Oct 2025).

Optimization proceeds jointly over both networks. The total loss is

$L(\theta, \phi) = L_f(\theta) + L_e(\phi)$

where $L_f(\theta)$ penalizes appearance reconstruction error and $L_e(\phi)$ maximizes pixel exposure and saturation only for well-exposed regions, with additional regularization to enforce smoothness in the exposure field (e.g., via differences $\|e_\phi(x) - e_\phi(x+\epsilon)\|^2$).

3. Training and Generalization Strategies

NExF leverages latent exposure conditioning for accelerated and stable training. Empirical results indicate training times up to three times faster than HDR-specific radiance fields or NeRF-W baselines while achieving significant jumps in reconstruction quality (Niemeyer et al., 9 Oct 2025). For example, on the HDRNeRF dataset, NExF improves PSNR from 39.07 (HDRNeRF baseline) to 42.54 for in-distribution exposures and from 37.53 to 38.36 for out-of-distribution exposures.

Generalization across scenes with sparse or variable exposure is further enhanced by frameworks such as partially observed neural processes (PONP), which use permutation-invariant encoders and global aggregation to condition neural fields on partial sensor observations (Gu et al., 2023). These methods bypass the inefficiencies of gradient-based meta-learning and avoid the parameter explosion of hypernetworks. Instead, a latent vector $z$ extracted from context data enables the decoder to predict field outputs at arbitrary coordinates via $p(y_t|x_t, \mathcal{C}) = D(x_t; z)$. This shared representation increases sample efficiency and allows for rapid adaptation or direct inference in novel scenarios.

4. Extensions to Diverse Modalities and Dynamical Systems

NExF principles admit extension to domains beyond static scene reconstruction. In interacting dynamical systems, neural fields have been deployed to discover latent force fields governing dynamics, with absolute-state-based neural fields fused into graph networks of local (equivariant) interactions (Kofinas et al., 2023). Here, the “exposure” is the field value inferred by the neural field, capturing global effects such as gravity, electrostatics, or social/road topology. The field is rotated into object-centric coordinate frames and incorporated into message passing for trajectory prediction, illustrating NExF’s flexibility for latent field discovery and interpretable dynamical modeling.

Other domains include event-based vision (e.g., E-3DGS (Yin et al., 22 Oct 2024)), where exposure events from controlled hardware transmittance yield grayscale images supplying strong guidance for 3D Gaussian splatting. Exposure and motion event separation enables robust, high-speed reconstruction even under extreme lighting or motion blur and is supported by new benchmarks (EME-3D) with explicit exposure event streams.

5. Representation, Embedding, and Downstream Applications

Recent advances in neural field embedding (e.g., nf2vec (Ramirez et al., 2023)) provide mechanisms to compress an entire field's parameters into a latent vector, facilitating deep learning on 3D NExF data. The encoder processes stacked parameter matrices via rowwise transformations and pooling, yielding a fixed-size, task-agnostic embedding for downstream pipelines including classification, retrieval, or generative modeling. Embeddings are robust across diverse field types—signed/unsigned distance, occupancy fields, and radiance/exposure fields—enabling unified handling of geometry and appearance. Challenges such as weight-space symmetry are mitigated by shared initialization across datasets, preserving meaningful clustering and enhancing parameter-space coherence.

Probabilistic methods (Geometric Neural Process Fields (Yin et al., 4 Feb 2025)) augment generalization using hierarchical latent variable models and spatially-structured geometric bases. Context observations are summarized into Gaussian bases and semantic embeddings, with both global and local latent variables capturing scene-level and coordinate-specific uncertainties. Such hierarchical modeling improves few-shot adaptation in novel view synthesis and delivers higher PSNR and log-likelihood scores for signal regression, demonstrating the utility of latent exposure field inference and spatial inductive bias.

6. Architectural and Optimization Considerations

Hyperparameter selection exerts significant influence over NExF quality and applicability. Shared initialization leads to semantically grouped parameters and increases downstream classification accuracy by as much as 100% (Papa et al., 2023). Overtraining decreases representation quality as the parameter vectors diverge; effective monitoring via off-grid reconstruction ratios (off-grid PSNR / on-grid PSNR) enables early stopping at optimal representation stages. Architectural choices such as hidden layer size, expressiveness (e.g., in SIREN or MFN networks), and dimensionality directly affect both reconstruction fidelity and the usefulness of the field as an embedding for downstream use.

Efficient large-scale optimization is supported by highly parallelized libraries, such as “fit‐a‐nef” (JAX-based), providing order-of-magnitude speed-ups and enabling practical hyperparameter sweeps and model selection—an essential capability for NExF training over extensive datasets (Neural Field Arena (Papa et al., 2023)).

7. Practical Impact, Future Directions, and Limitations

NExF achieves state-of-the-art performance for 3D scene reconstruction with spatially consistent appearance across diverse illumination conditions, outperforming exposure-blind frameworks and reducing training times. Applications span photorealistic scene synthesis, augmented reality, film and game content creation, and architectural visualization in HDR scenarios.

Potential enhancements include handling extreme low-light/overexposure via physical priors or extended photometric modeling, integrating with advanced neural scene representations for improved inference speed and quality, leveraging probabilistic uncertainty quantification for reliability-critical domains (e.g., biomedical imaging), and expanding to increasingly heterogeneous, large-scale real-world datasets.

Identified limitations include challenges in managing weight-space permutations in neural field embeddings, scalability of hierarchical latent models, robustness in ultra-low/high exposure regimes, and interpretability of the learned exposure field vis-à-vis physical scene parameters. The continued development of standardized benchmarks (Neural Field Arena, EME-3D), reproducible protocols, and shared libraries will be pivotal for future research and broader deployment.

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Neural Exposure Fields (NExF) represent a comprehensive extension of neural field representations for 3D scenes, unifying appearance and exposure while admitting scalable, robust, and efficient optimization independent of image-level capture artifacts. This principled architectural advancement opens opportunities for consistent and high-fidelity view synthesis across challenging real-world environments.