REFRAME: Reflective Surface Real-Time Rendering for Mobile Devices (2403.16481v2)

Abstract: This work tackles the challenging task of achieving real-time novel view synthesis for reflective surfaces across various scenes. Existing real-time rendering methods, especially those based on meshes, often have subpar performance in modeling surfaces with rich view-dependent appearances. Our key idea lies in leveraging meshes for rendering acceleration while incorporating a novel approach to parameterize view-dependent information. We decompose the color into diffuse and specular, and model the specular color in the reflected direction based on a neural environment map. Our experiments demonstrate that our method achieves comparable reconstruction quality for highly reflective surfaces compared to state-of-the-art offline methods, while also efficiently enabling real-time rendering on edge devices such as smartphones.

• The paper introduces a novel framework that decouples diffuse and specular components to achieve real-time rendering of reflective surfaces on mobile devices.
• It employs advanced mesh optimization with vertex and normal offset learning along with a learned environment feature map for enhanced visual and geometric accuracy.
• Experimental results demonstrate competitive performance against state-of-the-art methods, validating its applicability on both mobile and desktop platforms.

Decoupling Glossy Reflections for Real-Time Rendering on Mobile Devices with REFRAME

Introduction

Rendering highly reflective surfaces in real-time, especially on hardware-constrained platforms like mobile devices, poses a significant challenge in computer graphics and deep learning fields. Traditional real-time rendering methods often struggle to capture the detailed, view-dependent appearances characteristic of reflective objects. This limitation stems from the difficulties inherent in accurately modeling the interactions between light and reflective surfaces within the computational budget of mobile devices.

REFRAME (Reflective Surface Real-Time Rendering for Mobile Devices) addresses these issues by introducing a novel method that decouples diffuse and specular components, efficiently models specular reflections through a learned environment feature map, and optimizes the geometry of an initial mesh for enhanced visual fidelity. This approach enables the approximation of complex reflective phenomena within the constraints of mobile device hardware, delivering improvements in both rendering quality and performance.

Methodology

REFRAME's methodology revolves around a mesh-based rendering framework supplemented with advanced techniques for managing reflective appearances and geometric fidelity. The core components of this framework include:

Geometry Learning for Mesh Optimization

• Vertex Offset Learning: A geometry learner network optimizes vertex positions relative to an initial coarse mesh, enhancing the final rendered image's visual quality.
• Normal Offset Learning: Beyond vertex adjustments, the method also computes per-vertex normal offsets, leading to improved normal estimations that enhance specular reflection modeling without increasing the mesh complexity.

Reflective Surface Modeling

• Diffuse and Specular Decomposition: REFRAME decomposes the final color output into diffuse and specular components, where the latter is crucial for capturing the view-dependent effects characteristic of glossy surfaces.
• Environment Feature Map: A significant innovation in REFRAME is its approach to representing view-dependent effects through a learned environment feature map. This compact representation captures complex lighting interactions and can be efficiently queried in real-time, facilitating the rendering of reflective surfaces without heavy computational costs.
• Mesh-Based Acceleration: By leveraging the traditional graphics pipeline and distilling complex neural representations into mesh and texture formats, REFRAME achieves real-time performance across various platforms, including desktop and mobile devices.

Experimental Results

• Rendering Quality: Comparative evaluations with state-of-the-art methods like Ref-NeRF and others have shown REFRAME's superior ability to render highly reflective objects with remarkable fidelity. Notably, in object-centric scenes, REFRAME achieves competitive or superior rendering quality metrics (PSNR, SSIM, LPIPS) compared to both real-time and non-real-time methods.
• Performance Across Devices: REFRAME's rendering performance is evaluated across an assortment of devices, including MacBook Pro, iPhone 12, and others. Its computational efficiency, combined with low memory overhead, exemplifies its suitability for mobile platforms.
• Geometric Accuracy: The optimized meshes produced by REFRAME not only contribute to its rendering quality but also exhibit enhanced geometric accuracy, particularly in capturing the nuances of glossy objects.

Impact and Future Work

REFRAME represents a significant stride towards achieving high-quality rendering of reflective surfaces in real-time on mobile devices. By effectively decoupling the rendering equation into manageable components and introducing a novel environment feature map, it paves the way for future research into efficient, photorealistic rendering on constrained hardware.

Potential future directions include extending this framework to dynamic scenes, further reducing computational and memory overheads, and exploring adaptive techniques for mesh simplification that retain visual fidelity while maximizing rendering speed.

REFRAME's contributions to real-time rendering of reflective surfaces on mobile devices not only enhance the visual richness possible on these platforms but also broaden the horizons for deploying complex 3D graphics applications in everyday scenarios.