Neural Reflectance Fields for Appearance Acquisition (2008.03824v2)

Abstract: We present Neural Reflectance Fields, a novel deep scene representation that encodes volume density, normal and reflectance properties at any 3D point in a scene using a fully-connected neural network. We combine this representation with a physically-based differentiable ray marching framework that can render images from a neural reflectance field under any viewpoint and light. We demonstrate that neural reflectance fields can be estimated from images captured with a simple collocated camera-light setup, and accurately model the appearance of real-world scenes with complex geometry and reflectance. Once estimated, they can be used to render photo-realistic images under novel viewpoint and (non-collocated) lighting conditions and accurately reproduce challenging effects like specularities, shadows and occlusions. This allows us to perform high-quality view synthesis and relighting that is significantly better than previous methods. We also demonstrate that we can compose the estimated neural reflectance field of a real scene with traditional scene models and render them using standard Monte Carlo rendering engines. Our work thus enables a complete pipeline from high-quality and practical appearance acquisition to 3D scene composition and rendering.

• The paper introduces neural reflectance fields that combine scene geometry and reflectance properties using a deep neural network for realistic image rendering.
• The paper presents a differentiable ray marching framework that accurately models complex lighting effects, including shadows and specularities, under varied conditions.
• The paper demonstrates that the method outperforms traditional mesh-based and earlier learning approaches in both visual quality and computational efficiency.

Overview of "Neural Reflectance Fields for Appearance Acquisition"

The paper "Neural Reflectance Fields for Appearance Acquisition" introduces an innovative approach to scene representation through a concept known as neural reflectance fields. This technique employs a deep fully-connected neural network to encode key properties of a 3D scene, including volume density, normals, and reflectance, at any given point. The authors also present a physically-based differentiable ray marching framework that utilizes this representation to render images from novel viewpoints and lighting conditions. Notably, the method can be effectively applied using images captured with a simple collocated camera-light setup, making it a practical and high-quality solution for capturing complex real-world scenes.

Technical Advancements

1. Neural Reflectance Fields: The authors propose a novel scene representation that differs from prior works by integrating both scene geometry and reflectance properties in a continuous manner. This is achieved using a multi-layer perceptron (MLP), which calculates reflectance properties, normals, and volume density at any 3D location within the scene.
2. Differentiable Ray Marching Framework: The framework is based on volume rendering principles and enhances them by also including reflectance properties in the shading model, allowing for accurate rendering under various lighting conditions. This is crucial for modeling realistic specularities, shadows, and other complex light interactions.
3. Efficient Acquisition and Rendering: The method can acquire neural reflectance fields from images captured with a standard phone camera and flash. This setup is both practical and effective, despite the sparse sampling of appearance across the view-light space. Moreover, the authors develop an adaptive transmittance volume to precompute light effects like shadows, optimizing the rendering process.
4. Integration with Standard Graphics Engines: The neural reflectance fields are designed to be compatible with traditional Monte Carlo rendering engines, presenting a significant advantage by allowing integration with existing 3D models and supporting comprehensive light transport analysis. The ability to merge neural fields with standard models in a renderer like Mitsuba highlights its versatility.

Empirical Results

The research demonstrates that neural reflectance fields outperform traditional mesh-based methods and previous learning-based approaches in terms of both visual quality and computational efficiency. The method is capable of capturing high-frequency details and reproducing complex scene characteristics that other methods typically miss, such as accurate specular and shadow details.

Implications and Future Research

The introduction of neural reflectance fields could influence future studies in scene acquisition, enabling practical and easy-to-use capture systems that provide high-quality representations suitable for various applications, including virtual reality and special effects. Moreover, the method's capability to work with different reflectance models and integrate into conventional graphics pipelines presents opportunities for expanding current digital content creation processes.

In future research, it would be interesting to investigate the scalability of this method to larger scenes and dynamic environments, as well as exploring its integration with other neural rendering techniques. Potential advancements in neural reflectance fields could offer significant improvements in digital reconstruction and visual representation, pushing the boundaries of realism in computational graphics.