HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces (2312.03160v2)

Abstract: Neural radiance fields provide state-of-the-art view synthesis quality but tend to be slow to render. One reason is that they make use of volume rendering, thus requiring many samples (and model queries) per ray at render time. Although this representation is flexible and easy to optimize, most real-world objects can be modeled more efficiently with surfaces instead of volumes, requiring far fewer samples per ray. This observation has spurred considerable progress in surface representations such as signed distance functions, but these may struggle to model semi-opaque and thin structures. We propose a method, HybridNeRF, that leverages the strengths of both representations by rendering most objects as surfaces while modeling the (typically) small fraction of challenging regions volumetrically. We evaluate HybridNeRF against the challenging Eyeful Tower dataset along with other commonly used view synthesis datasets. When comparing to state-of-the-art baselines, including recent rasterization-based approaches, we improve error rates by 15-30% while achieving real-time framerates (at least 36 FPS) for virtual-reality resolutions (2Kx2K).

• The paper introduces HybridNeRF, which integrates surface and volumetric representations to reduce samples per ray and improve error rates by 15-30%.
• It employs spatially adaptive parameters and strategies like weighted Eikonal loss and proposal network baking to optimize rendering speed and accuracy.
• Evaluations demonstrate real-time performance exceeding 36 FPS at 2K resolution, making it promising for immersive AR/VR applications.

HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces

The paper "HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces" introduces HybridNeRF, a method combining surface and volumetric representations to enhance performance and quality in neural rendering, specifically for real-time applications.

Introduction

Neural Radiance Fields (NeRFs) have established themselves as the state-of-the-art for photorealistic novel view synthesis. Despite their superior rendering quality, NeRFs suffer from significant computational demands due to their dependence on volume rendering, necessitating numerous samples per ray. This paper identifies that most real-world objects can be efficiently modeled using surfaces, reducing the number of samples per ray. However, strictly using surface representations like Signed Distance Functions (SDFs) often fails with semi-opaque or thin structures.

Methodology

Hybrid Representation

HybridNeRF leverages the strengths of both surface and volumetric methods by rendering the majority of the scene as surfaces while modeling complex regions volumetrically. The method starts with a surface-based representation akin to SDFs, improving geometric accuracy, and then transitions to a volumetric representation where necessary.

Surfaceness and Eikonal Loss

A crucial aspect of HybridNeRF is the concept of "surfaceness," represented by the parameter $\beta$. Higher $\beta$ values transform surface representations towards binary occupancy, increasing rendering efficiency but potentially degrading quality in challenging regions. Unlike previous methods that use a global $\beta$, HybridNeRF introduces spatially adaptive $\beta$ values, allowing the model to handle different scene complexities appropriately.

Efficiency Improvements

HybridNeRF implements several strategies to improve rendering efficiency without compromising quality:

1. Weighted Eikonal Loss: By adjusting the Eikonal loss based on distance, the model can maintain high-quality geometric reconstructions in the foreground while treating backgrounds more flexibly.
2. Proposal Network Baking: The proposal network, used to identify sample locations efficiently, is baked into a binary occupancy grid for faster lookups during rendering.
3. MLP Distillation and Hardware Texture Interpolation: The model's large MLPs are distilled into smaller networks, and features are stored in hardware-optimized textures, significantly speeding up rendering.

Results and Evaluation

Datasets and Baselines

HybridNeRF was evaluated against multiple datasets, including the Eyeful Tower dataset designed for VR applications and the widely-used MipNeRF-360 dataset. Comparisons were made with several existing methods, including iNGP, VR-NeRF, MERF, and the recent 3D Gaussian Splatting method.

Performance

HybridNeRF achieved state-of-the-art performance, improving error rates by 15-30% over baselines while maintaining real-time frame rates exceeding 36 FPS at 2K × 2K resolution. It rendered almost 10x faster than some methods, providing higher fidelity outcomes. Critical comparisons highlighted HybridNeRF's ability to accurately model challenging regions like reflections and shadows, which other fast-rendering methods struggled with.

Implications and Future Directions

The presented approach of combining surface and volume representations holds significant promise for real-time applications such as augmented reality (AR) and virtual reality (VR) teleconferencing. The ability to render high-fidelity images in real-time opens up broader use cases in immersive experiences.

Future research could explore further optimization of the balance between surface and volumetric elements, as well as potential integration with splatting-based methods to bridge the existing speed gap. Moreover, introducing hardware acceleration primitives more broadly could offer additional performance gains.

Conclusion

HybridNeRF represents a significant advancement in neural rendering technologies by efficiently combining surface and volumetric representations, achieving real-time rendering speeds with high visual fidelity. This balance makes it well-suited for practical, immersive applications and sets a new benchmark for future developments in the field.