Aleth-NeRF: Illumination Adaptive NeRF with Concealing Field Assumption
(2312.09093)Abstract
The standard Neural Radiance Fields (NeRF) paradigm employs a viewer-centered methodology, entangling the aspects of illumination and material reflectance into emission solely from 3D points. This simplified rendering approach presents challenges in accurately modeling images captured under adverse lighting conditions, such as low light or over-exposure. Motivated by the ancient Greek emission theory that posits visual perception as a result of rays emanating from the eyes, we slightly refine the conventional NeRF framework to train NeRF under challenging light conditions and generate normal-light condition novel views unsupervised. We introduce the concept of a "Concealing Field," which assigns transmittance values to the surrounding air to account for illumination effects. In dark scenarios, we assume that object emissions maintain a standard lighting level but are attenuated as they traverse the air during the rendering process. Concealing Field thus compel NeRF to learn reasonable density and colour estimations for objects even in dimly lit situations. Similarly, the Concealing Field can mitigate over-exposed emissions during the rendering stage. Furthermore, we present a comprehensive multi-view dataset captured under challenging illumination conditions for evaluation. Our code and dataset available at https://github.com/cuiziteng/Aleth-NeRF
Overview
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Aleth-NeRF introduces a 'Concealing Field' to model diminished visibility due to poor lighting, enhancing NeRF's ability to render 3D scenes.
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The new method improves the quality and consistency of images generated under extreme lighting conditions, without relying on post-processing.
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Aleth-NeRF's performance surpasses existing enhancement and exposure correction techniques, particularly in rendering seamless and realistic views.
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The framework offers a new dataset for testing under varying lighting conditions and benchmarks against other enhancement methods.
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Aleth-NeRF faces limitations like scene-specific training and may struggle with non-uniform lighting, indicating areas for future research.
Introduction
Neural Radiance Fields (NeRF) provide a powerful tool to construct high-fidelity 3D scenes from a set of 2D photographs. The technique is well-regarded for its ability to capture detailed geometry and realistically render novel views. However, a notable drawback of standard NeRF implementations is their limited effectiveness in extreme lighting conditions, such as low light or overexposure. In these challenging environments, NeRF's assumptions can fail, resulting in poor reconstruction and rendering quality.
Enhancing NeRF for Extreme Lighting
To address the challenges in adverse lighting, a new system called Aleth-NeRF has been introduced. Aleth-NeRF differentiates itself by incorporating a "Concealing Field", a novel conception drawn from ancient Greek philosophy, which hypothesizes that the visibility of objects is reduced by a lack of illumination around them rather than intrinsic object properties. The key innovation in Aleth-NeRF is assigning transmittance values to particles, akin to attribute reduced light transmission to the surrounding air, thus modeling how objects might appear under suboptimal lighting conditions.
Methodology
For constructing its improved renditions, Aleth-NeRF estimates the density and color of objects even under low lighting or overexposure by first learning a volumetric scene representation with said concealing fields during training. This enables the system to render these scenes closer to their appearance under normal lighting. For low-light environments, the concealing fields are removed in the rendering stage, revealing the scene as it would appear with adequate illumination. Conversely, for overexposed scenes, concealing fields are added during rendering to compensate for excessive brightness and restore normal lighting appearance.
Performance and Contributions
The performance of Aleth-NeRF is conclusively robust across multiple scenarios. When faced with the complexities of low-light and over-exposed environments:
- Aleth-NeRF demonstrates convincing capabilities in enhancing image quality and ensuring 3D consistency across different views.
- Compared to existing enhancement and exposure correction techniques and combinations thereof, Aleth-NeRF emerges superior in generating seamless, realistic imagery.
- It realizes this without relying on post-processing, instead training end-to-end directly on SRGB images -- both under- and over-exposed.
Additionally, the framework contributes:
- A challenging illumination multi-view dataset, which includes paired SRGB low-light, normal-light, and over-exposed images.
- Comparisons with various image enhancement and exposure correction methods, demonstrating Aleth-NeRF's higher quality and consistency in rendering novel views.
Conclusions
In summation, Aleth-NeRF offers a valuable advancement in rendering 3D scenes under challenging lighting conditions, with implications for both image enhancement and numerous potential applications in computer vision and graphics. Despite its novelty and strengths, the system does inherit a limitation from generic NeRF models: the necessity for scene-specific training. Furthermore, the model may face potential difficulties in scenes with non-uniform light or shadows, which are areas earmarked for future improvement. Nevertheless, Aleth-NeRF's approach symbolizes a significant step forward for neural rendering in the presence of real-world lighting variations.
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