Adaptive Shells for Efficient Neural Radiance Field Rendering
(2311.10091)Abstract
Neural radiance fields achieve unprecedented quality for novel view synthesis, but their volumetric formulation remains expensive, requiring a huge number of samples to render high-resolution images. Volumetric encodings are essential to represent fuzzy geometry such as foliage and hair, and they are well-suited for stochastic optimization. Yet, many scenes ultimately consist largely of solid surfaces which can be accurately rendered by a single sample per pixel. Based on this insight, we propose a neural radiance formulation that smoothly transitions between volumetric- and surface-based rendering, greatly accelerating rendering speed and even improving visual fidelity. Our method constructs an explicit mesh envelope which spatially bounds a neural volumetric representation. In solid regions, the envelope nearly converges to a surface and can often be rendered with a single sample. To this end, we generalize the NeuS formulation with a learned spatially-varying kernel size which encodes the spread of the density, fitting a wide kernel to volume-like regions and a tight kernel to surface-like regions. We then extract an explicit mesh of a narrow band around the surface, with width determined by the kernel size, and fine-tune the radiance field within this band. At inference time, we cast rays against the mesh and evaluate the radiance field only within the enclosed region, greatly reducing the number of samples required. Experiments show that our approach enables efficient rendering at very high fidelity. We also demonstrate that the extracted envelope enables downstream applications such as animation and simulation.
Overview
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Neural Radiance Fields (NeRFs) offer high photorealism but require intensive computation.
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A new method transitions between volumetric and surface-based rendering to improve efficiency.
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An explicit mesh 'shell' guides sampling, focusing on essential parts of the scene.
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The adaptive shell supports animations and simulations by acting as a proxy for dynamic transformations.
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The approach speeds up rendering and retains visual quality, with potential for real-time applications.
Overview of Neural Radiance Fields
Understanding the intricacies of Neural Radiance Fields (NeRFs) is imperative for various applications, from creating realistic graphics in virtual simulations to generating novel viewpoints in complex scenes. NeRFs provide a revolutionary approach to rendering, with impressive photorealism, by modeling scenes as continuous volumetric fields. Despite their significant visual quality, the computational demands of NeRFs are high due to the volumetric nature of rendering, which has traditionally required extensive sampling to achieve high-resolution images.
Optimizing NeRF Rendering
Addressing the computational challenges, researchers have developed a fresh approach, adaptively transitioning between volumetric and surface-based rendering to accelerate the process. This method is rooted in the observation that while some scene regions, like fuzzy or intricate geometries, benefit from exhaustively rendered volumes, others such as smooth opaque surfaces are optimally represented with fewer samples. An explicit mesh envelope, termed "shell", is introduced, spatially bounding the volumetric representation and subsequently serving as a guide for sampling.
Implementation and Applications
By utilizing a neural network, a representation for the 3D density and appearance of a scene is constructed. However, instead of a uniformly defined kernel size across the scene, the new method proposes a spatially varying kernel. This kernel adeptly broadens or narrows depending on the scene's complexity, paving the way for the dynamic mesh envelope to encompass the essential rendering region efficiently.
The explicit mesh shell not only fosters a more streamlined rendering process but also opens up opportunities for the model to be directly useful in animation and simulation pipelines. It conveniently acts as a proxy for applying algorithms for deformations or suffering physical interactions, maintaining visual fidelity throughout dynamic transformations.
Findings and Future Potential
The experiments conducted confirm the effectiveness of this new approach, demonstrating enhanced rendering speeds without compromising visual detail across diverse data sets. Furthermore, this study has sparked interest in future research that could combine the strengths of this method with other high-performance techniques, potentially achieving real-time performance on standard consumer hardware while retaining high visual quality.
In summary, this study offers a pivotal step toward achieving the elusive goal of efficient rendering of neural radiance fields without compromising on quality, making it markedly relevant for real-time applications in computer graphics and beyond.
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