NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis (2012.03927v1)

Abstract: We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions. Our method represents the scene as a continuous volumetric function parameterized as MLPs whose inputs are a 3D location and whose outputs are the following scene properties at that input location: volume density, surface normal, material parameters, distance to the first surface intersection in any direction, and visibility of the external environment in any direction. Together, these allow us to render novel views of the object under arbitrary lighting, including indirect illumination effects. The predicted visibility and surface intersection fields are critical to our model's ability to simulate direct and indirect illumination during training, because the brute-force techniques used by prior work are intractable for lighting conditions outside of controlled setups with a single light. Our method outperforms alternative approaches for recovering relightable 3D scene representations, and performs well in complex lighting settings that have posed a significant challenge to prior work.

• The paper introduces NeRV, a novel method using MLPs to model complex lighting and reduce computational complexity in 3D rendering.
• It efficiently simulates both direct and indirect light transport to achieve superior photorealistic view synthesis.
• NeRV’s advanced visibility modeling accurately renders shadows and illumination, paving the way for dynamic virtual media applications.

An Analysis of NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis

The paper introduces a novel approach called NeRV (Neural Reflectance and Visibility Fields) aimed at addressing the long-standing problem in computer vision and computer graphics of creating photorealistic 3D models from standard photos. Unlike previous models, NeRV can generate 3D representations of scenes that allow for both novel view synthesis and relighting under arbitrary lighting conditions. The significance of NeRV lies in its ability to efficiently simulate both direct and indirect light transport in a scene, facilitating rendering in complex illumination scenarios that have traditionally posed challenges.

Methodology

NeRV characterizes a scene using a continuous volumetric function parameterized through multi-layer perceptrons (MLPs). For any given 3D location in the scene, these MLPs provide a set of properties: volume density, surface normals, BRDF material parameters, distance to the first surface intersection, and environmental visibility. The MLPs enable NeRV to simulate complex lighting effects by incorporating visibility and surface intersection fields, which are crucial for handling both direct and indirect illumination, unlike prior NeRF-related models which are typically constrained to simple lighting setups.

One of the critical innovations in NeRV is the use of a "visibility" MLP that models visibility as a continuous function, thus avoiding the computational expense of brute-force calculations of light visibility, which are impractical in real-world complex lighting environments. This improvement allows NeRV to dramatically reduce the computational complexity encountered in volume rendering integrals, making it feasible to use during training and optimization.

Numerical Results and Validation

In a range of experiments, NeRV demonstrates superior performance to prior models, such as the method by Bi et al., specifically in scenarios requiring complex lighting simulations. When trained under conditions with ambient lighting and colorful light sources, NeRV surpasses baseline methods that struggle with similar tasks due to their limited ability to model indirect light effect. The accurate rendering of shadows and illumination variations in NeRV's results support its efficacy in achieving high visual fidelity.

Implications and Future Developments

The development of NeRV represents a notable advancement in rendering technology, particularly in its enhanced capacity for handling lighting variations without exorbitant computational cost. Potential applications of NeRV span across fields requiring dynamic and photorealistic 3D scenes, such as virtual reality, film production, and interactive gaming, where lighting plays a crucial role in visual realism.

From a theoretical standpoint, NeRV's architecture suggests new possibilities for improving neural representations of 3D scenes. It provides a pathway for integrating complex physical models of lighting and visibility into neural network-based systems, offering a more robust alternative to traditional rendering techniques.

Looking forward, further research could investigate optimizing the training process of MLPs to enhance efficiency further, possibly incorporating more advanced neural architectures or incorporating more physics-based rendering models to broaden the applicability of NeRV. The exploration of NeRV's adaptability to real-world data, beyond the controlled synthetic data on which it was validated, would present a substantial forward leap.

In summary, NeRV stands as a significant advancement in the area of photorealistic relightable view synthesis. By mitigating the computational complexity involved in modeling complex lighting environments, it paves the way for more realistic and dynamic 3D scene renderings that could transform various aspects of digital visualization and interactive media.