IRGS: Inter-Reflective Gaussian Splatting with 2D Gaussian Ray Tracing (2412.15867v2)

Abstract: In inverse rendering, accurately modeling visibility and indirect radiance for incident light is essential for capturing secondary effects. Due to the absence of a powerful Gaussian ray tracer, previous 3DGS-based methods have either adopted a simplified rendering equation or used learnable parameters to approximate incident light, resulting in inaccurate material and lighting estimations. To this end, we introduce inter-reflective Gaussian splatting (IRGS) for inverse rendering. To capture inter-reflection, we apply the full rendering equation without simplification and compute incident radiance on the fly using the proposed differentiable 2D Gaussian ray tracing. Additionally, we present an efficient optimization scheme to handle the computational demands of Monte Carlo sampling for rendering equation evaluation. Furthermore, we introduce a novel strategy for querying the indirect radiance of incident light when relighting the optimized scenes. Extensive experiments on multiple standard benchmarks validate the effectiveness of IRGS, demonstrating its capability to accurately model complex inter-reflection effects.

• The paper introduces IRGS, a novel framework using 2D Gaussian ray tracing that accurately models inter-reflections in inverse rendering.
• The paper employs a two-stage process, starting with 2D Gaussian pretraining for geometry alignment and followed by inverse rendering optimization for material and lighting estimation.
• Experimental results on datasets like Synthetic4Relight and TensoIR demonstrate IRGS's superior performance in relighting tasks and precise material estimation compared to existing methods.

Inter-Reflective Gaussian Splatting with 2D Gaussian Ray Tracing for Inverse Rendering

The paper presents Inter-Reflective Gaussian Splatting (IRGS), a novel framework for inverse rendering that leverages a differentiable 2D Gaussian ray tracing technique to accurately model inter-reflection effects. By addressing the limitations of previous approaches, IRGS offers significant advancements in the accurate estimation of materials, lighting, and geometry.

Overview

In the field of inverse rendering, reconstructing accurate geometry, material, and lighting from 2D images is a complex issue that has spawned numerous approaches. Traditional Neural Radiance Field (NeRF) methods, despite offering high-quality reconstruction, are often constrained by inefficiencies and limitations in representing ray-based effects. The recent advent of 3D Gaussian Splatting (3DGS) has shown promise in addressing some of these shortcomings, offering improved efficiency and rendering quality by modeling scenes as collections of 3D Gaussians. However, these methods face significant challenges in simulating the full spectrum of light interactions, particularly when it comes to inter-reflective effects.

Methodology

IRGS seeks to combine the efficiency of Gaussian splatting with the capability to simulate complex inter-reflective light interactions. It tackles the problem by employing a differentiable 2D Gaussian ray tracing technique. This method allows for direct ray tracing across 2D Gaussian primitives, facilitating efficient and precise ray-splat intersection computations. By utilizing this technique, IRGS can compute both visibility and indirect radiance of incident light dynamically, thus enabling the inclusion of the complete rendering equation without the need for simplifications.

Technique and Implementation

IRGS introduces a novel 2D Gaussian ray tracing approach that implements the rendering equation directly post-rasterization. This approach, distinct from prior methods that perform shading on individual Gaussian primitives or apply a simplified rendering equation, enhances the accuracy of material and light estimations. The ray tracing is realized through adaptive icosahedron meshes, which are transformed to match the 2D Gaussian primitives, allowing for precise intersection points to be determined.

Within the training framework, IRGS is structured into a two-stage process:

1. 2D Gaussian Pretraining: This initial phase focuses on ensuring robust geometry and normal map alignment, supported by a set of losses including depth distortion and normal consistency.
2. Inverse Rendering Optimization: The framework transitions to optimizing material and lighting parameters, leveraging stratified sampling for rendering equation evaluation with an emphasis on indirect radiance computations made feasible by 2D Gaussian ray tracing.

Experimental Results

IRGS was evaluated against existing methods on various datasets such as Synthetic4Relight and TensoIR. Experimental results demonstrate that IRGS achieves superior performance in relighting tasks, showing remarkable accuracy in estimating materials and environment lighting. The results attest to the validity of IRGS's approach in capturing complex inter-reflective effects that competing methods struggle to represent accurately.

Implications and Future Directions

The implications of IRGS are significant for computer graphics and vision fields, particularly in enhancing practical applications like photorealistic scene reconstruction and virtual reality. From a theoretical standpoint, the approach advances the understanding of integrating Gaussian-based scene representations with deep learning frameworks.

Future research may extend IRGS by exploring dynamic scenes and real-time applications, leveraging its efficiency and the flexibility provided by its differentiable ray tracing model. Moreover, improvements in hardware acceleration, such as further optimization of the Bounding Volume Hierarchy (BVH) construction or refined sorting mechanisms, could amplify the efficiency of the ray tracing process.

In conclusion, IRGS sets a new benchmark in inverse rendering methodologies by seamlessly integrating complex light interactions into Gaussian-based representations, providing a robust and accurate framework for material and lighting estimation.