Volumetric Surfaces: Representing Fuzzy Geometries with Multiple Meshes (2409.02482v1)

Abstract: High-quality real-time view synthesis methods are based on volume rendering, splatting, or surface rendering. While surface-based methods generally are the fastest, they cannot faithfully model fuzzy geometry like hair. In turn, alpha-blending techniques excel at representing fuzzy materials but require an unbounded number of samples per ray (P1). Further overheads are induced by empty space skipping in volume rendering (P2) and sorting input primitives in splatting (P3). These problems are exacerbated on low-performance graphics hardware, e.g. on mobile devices. We present a novel representation for real-time view synthesis where the (P1) number of sampling locations is small and bounded, (P2) sampling locations are efficiently found via rasterization, and (P3) rendering is sorting-free. We achieve this by representing objects as semi-transparent multi-layer meshes, rendered in fixed layer order from outermost to innermost. We model mesh layers as SDF shells with optimal spacing learned during training. After baking, we fit UV textures to the corresponding meshes. We show that our method can represent challenging fuzzy objects while achieving higher frame rates than volume-based and splatting-based methods on low-end and mobile devices.

• The paper introduces a multi-layer mesh approach that bounds sampling locations to enable efficient real-time rendering of fuzzy geometries.
• It employs rasterization-based sampling combined with loss functions and texture fitting to optimize layer smoothness and performance.
• This method bridges the gap between speed and fidelity, making high-quality rendering feasible on low-end and mobile hardware for AR/VR and gaming.

Volumetric Surfaces: Representing Fuzzy Geometries with Multiple Meshes

The paper "Volumetric Surfaces: Representing Fuzzy Geometries with Multiple Meshes" addresses the challenges of real-time view synthesis, particularly on low-end or mobile devices, by introducing a novel representation method that efficiently models fuzzy geometries.

Problem Statement

Traditional methods for real-time rendering fall into three categories: surface-based, volume-based, and splatting. Surface-based methods, while fast, are inadequate for representing fuzzy geometries such as hair. Volume-based methods provide higher fidelity for such geometries but suffer from high computational and memory overheads. Splatting methods fall somewhere in between but have their own set of complexities, particularly related to sorting input primitives. The paper identifies three primary problems with existing techniques:

1. Number of Sampling Locations (P1): Volume-based methods necessitate an unbounded number of samples per ray.
2. Sampling Efficiency (P2): Volume rendering requires additional overhead due to empty space skipping.
3. Sorting Input Primitives (P3): Splatting methods demand sorting, which is computationally expensive.

Proposed Solution

The authors introduce Volumetric Surfaces, a method that represents objects as semi-transparent multi-layer meshes. This approach aims to:

1. Bound the Number of Sampling Locations (P1): By using only a few layers (between three to nine), the required number of samples per ray is minimized and bounded.
2. Efficient Sampling Locations (P2): Sampling locations are found via rasterization, a process that bypasses the need for additional empty space skipping.
3. Sorting-Free Rendering (P3): The layers are rendered in a fixed order, eliminating the need for sorting.

The Volumetric Surfaces method integrates several technical components:

• Multiple Semi-Transparent Layers: Objects are represented as several layers of semi-transparent surfaces (SDF shells), each optimized for spacing through the training phase.
• Rasterization-Based Sampling: The layers are rasterized in a fixed order, contributing to faster rendering times.
• Baked Textures: After training, the method includes fitting UV textures to the corresponding meshes, making the representation compact and efficient for practical hardware.

Technical Details

Representation and Training

• Layer Initialization: Training starts with a single opaque SDF to prevent degenerate solutions, followed by the addition of multiple layers.
• Loss Functions: The method employs Eikonal and curvature losses to ensure smooth and non-intersecting layers.
• Hierarchical Sampling: Hierarchical sampling improves efficiency by focusing samples around the depth values along rays.
• Mesh Simplification: The multi-layer meshes are simplified using established techniques to ensure they are lightweight and suitable for hardware-accelerated rasterization.

Performance and Results

Quantitative results across a variety of datasets indicate that Volumetric Surfaces perform significantly better than traditional surface-based methods in terms of representing fuzzy geometries. Notably:

• Image Quality: Achieves high PSNR and SSIM values, comparable to volume-based methods but with fewer resource demands.
• Frame Rates: Outperforms volume-based methods in rendering speed, particularly on lower-end devices.
• Memory Footprint: Keeps the memory footprint manageable, suited for mobile hardware.

For instance, a 7-Mesh configuration strikes an optimal balance, offering better image quality and faster rendering compared to fewer or more layers.

Implications and Future Directions

The introduction of Volumetric Surfaces marks a significant step toward enabling high-quality real-time rendering on devices with limited computational resources. This approach is particularly promising for applications in mobile VR/AR and gaming, where maintaining high frame rates is crucial.

Theoretically, this work bridges a gap between the speed of surface-based methods and the fidelity of volume-based methods. Practically, it makes it feasible to represent complex, semi-transparent geometries efficiently.

Future research could focus on developing an end-to-end training pipeline that directly outputs assets optimized for real-time rendering. Another potential direction could involve extending the method to handle even more complex geometries and textures, perhaps by integrating more advanced neural rendering techniques.

Conclusion

Volumetric Surfaces provide an efficient, robust method for representing and rendering fuzzy geometries on low-end or mobile devices. The combination of semi-transparent multi-layer meshes with rasterization-based sampling and optimized texture fitting ensures high image quality, faster rendering times, and a manageable memory footprint. This work represents a substantial contribution to the field of real-time rendering, particularly for applications requiring efficient handling of complex and fuzzy geometries.