MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures (2208.00277v5)

Abstract: Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize images of 3D scenes from novel views. However, they rely upon specialized volumetric rendering algorithms based on ray marching that are mismatched to the capabilities of widely deployed graphics hardware. This paper introduces a new NeRF representation based on textured polygons that can synthesize novel images efficiently with standard rendering pipelines. The NeRF is represented as a set of polygons with textures representing binary opacities and feature vectors. Traditional rendering of the polygons with a z-buffer yields an image with features at every pixel, which are interpreted by a small, view-dependent MLP running in a fragment shader to produce a final pixel color. This approach enables NeRFs to be rendered with the traditional polygon rasterization pipeline, which provides massive pixel-level parallelism, achieving interactive frame rates on a wide range of compute platforms, including mobile phones.

• The paper introduces MobileNeRF, a novel method that converts neural radiance fields into textured polygon meshes for enhanced mobile rendering.
• The paper implements a two-stage pipeline combining z-buffer rasterization with a lightweight MLP, achieving a 10x speed improvement and lower memory usage.
• The paper validates its approach on diverse datasets, demonstrating comparable quality to state-of-the-art methods and enabling real-time 3D visualization on mobile devices.

Exploiting Polygon Rasterization for Efficient Neural Field Rendering on Mobile Devices

The paper "MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures" addresses a significant challenge in rendering Neural Radiance Fields (NeRFs) on mobile devices. NeRFs are instrumental in generating photorealistic novel views from 3D scenes, but traditional implementations are not suited for widely available graphics hardware due to reliance on volumetric rendering techniques. This research introduces MobileNeRF, a new approach leveraging the polygon rasterization pipeline, thus making real-time rendering feasible on mobile architectures.

Summary of the Approach

MobileNeRF introduces a shift from traditional volumetric rendering to a pipeline that maps NeRFs onto textured polygonal meshes. This conversion facilitates the use of standard graphics hardware, which inherently supports polygon rasterization with z-buffer techniques. The core methodology is built around representing NeRF scenes using meshes where each polygon is associated with a texture atlas containing binary opacities and feature vectors. Rendering is performed across two stages:

1. Stage 1: Rasterizing the mesh into a feature image using traditional z-buffer techniques, which simplifies the scene into a 2D representation containing essential information for final rendering.
2. Stage 2: Employing a lightweight multi-layer perceptron (MLP) within a GLSL fragment shader to convert the feature image into final RGB pixel colors, ensuring rapid view-dependent shading.

These steps utilize the parallelism of contemporary graphics hardware, achieving interactive frame rates previously unattainable with NeRFs on mobile platforms.

Key Contributions and Observations

MobileNeRF's ability to harness widely available polygon rasterization pipelines results in substantial performance enhancements:

• MobileNeRF demonstrates a 10x speed improvement over existing state-of-the-art methods such as SNeRG, while maintaining output quality.
• The model significantly reduces memory consumption by utilizing surface textures rather than volumetric textures, allowing it to efficiently operate on devices with limited GPU resources, such as mobile phones.
• The implementation is lightweight enough to run directly in a web browser, enhancing accessibility and usability across different platforms.

The research also outlines the process of transitioning from a continuous opacity representation to a discrete polygonal mesh, allowing for efficient execution on commodity graphics processors. This development is pivotal as it decouples real-time rendering performance from hardware-specific capabilities, democratizing NeRF visualization to an extent.

Experimental Results and Implications

The experimental evaluations conducted on various datasets, including Synthetic 360° and forward-facing scenes, as well as unbounded outdoor environments, assert that MobileNeRF achieves comparable rendering quality to traditional methods while drastically improving speed and reducing hardware requirements. The evaluations cover multiple mobile platforms, underscoring the model's versatility and real-world applicability.

Given these advancements, MobileNeRF holds significant potential for practical applications in areas such as augmented reality, gaming, and real-time 3D reconstruction. Additionally, the methodology can stimulate further research in optimizing neural field representations for constrained computational environments.

Future Directions

Future research may explore extending MobileNeRF by integrating more sophisticated lighting models or supporting dynamic scenes with moving objects. There's also potential in exploring more efficient mesh generation techniques or compression methods to further reduce storage requirements without sacrificing quality or performance.

In conclusion, MobileNeRF represents a meaningful advancement in the deployment of neural radiance fields on mobile devices, offering new avenues for high-quality, real-time 3D rendering on ubiquitous hardware.