GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering (2410.24204v2)

Abstract: We consider the problem of physically-based inverse rendering using 3D Gaussian Splatting (3DGS) representations. While recent 3DGS methods have achieved remarkable results in novel view synthesis (NVS), accurately capturing high-fidelity geometry, physically interpretable materials and lighting remains challenging, as it requires precise geometry modeling to provide accurate surface normals, along with physically-based rendering (PBR) techniques to ensure correct material and lighting disentanglement. Previous 3DGS methods resort to approximating surface normals, but often struggle with noisy local geometry, leading to inaccurate normal estimation and suboptimal material-lighting decomposition. In this paper, we introduce GeoSplatting, a novel hybrid representation that augments 3DGS with explicit geometric guidance and differentiable PBR equations. Specifically, we bridge isosurface and 3DGS together, where we first extract isosurface mesh from a scalar field, then convert it into 3DGS points and formulate PBR equations for them in a fully differentiable manner. In GeoSplatting, 3DGS is grounded on the mesh geometry, enabling precise surface normal modeling, which facilitates the use of PBR frameworks for material decomposition. This approach further maintains the efficiency and quality of NVS from 3DGS while ensuring accurate geometry from the isosurface. Comprehensive evaluations across diverse datasets demonstrate the superiority of GeoSplatting, consistently outperforming existing methods both quantitatively and qualitatively.

• The paper introduces a hybrid representation that integrates isosurface extraction with 3D Gaussian splatting to improve geometry reconstruction and material decomposition.
• It leverages differentiable PBR equations for precise normal estimation, enabling end-to-end training in inverse rendering tasks.
• Experiments demonstrate significant gains in view synthesis quality and rendering speed across synthetic and real-world datasets.

Overview of GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-Based Inverse Rendering

The paper introduces GeoSplatting, an innovative approach to inverse rendering that leverages a novel hybrid representation, merging 3D Gaussian Splatting (3DGS) with explicit geometric guidance and differentiable physically-based rendering (PBR) equations. Despite recent progress in 3DGS methods focused on novel view synthesis (NVS), challenges persist in accurately capturing high-fidelity geometry and disentangling physically-interpretable materials and lighting. GeoSplatting addresses these challenges by incorporating explicit geometric guidance to facilitate precise normal modeling and material decomposition.

Technical Contributions

The primary contribution of this work is the development of a hybrid representation that integrates isosurface extraction with 3DGS. This integration enables the GeoSplatting method to enhance the accuracy of geometry reconstruction and material-lighting separation, key elements in solving inverse rendering problems. The main steps involved are:

1. Hybrid Representation: The approach first extracts isosurface mesh from a scalar field, converting it into 3DGS points. This allows for precise surface normal modeling, crucial for accurate PBR.
2. Differentiable PBR Framework: In addition to normal estimation, GeoSplatting incorporates differentiable PBR equations formulated for the 3DGS points. This fully differentiable framework assists in material decomposition and enables end-to-end training.
3. Efficiency: GeoSplatting retains the NVS efficiency of 3DGS, while simultaneously enhancing the quality of geometry and material capture.

Experimental Evaluation

The authors conduct rigorous evaluation across various datasets, showcasing that GeoSplatting consistently outperforms state-of-the-art methods both quantitatively and qualitatively. In novel view synthesis, GeoSplatting achieves remarkable improvements in PSNR and rendering times. The paper details comprehensive experiments on the NeRF Synthetic dataset, Synthetic4Relight dataset, and real-world DTU dataset, where GeoSplatting demonstrates superior performance in terms of geometry recovery, material decomposition, and relighting effects.

Implications and Future Directions

GeoSplatting represents a substantial contribution to the field of inverse rendering, offering a robust framework for integrating geometric precision with efficient rendering techniques. By bridging isosurface extraction and Gaussian splatting, this approach provides a promising pathway for further advancements in photorealistic rendering applications, such as VR/AR environments, gaming, and film production.

The paper underscores potential avenues for future research. One area is the extension of GeoSplatting to accommodate more complex, scene-level inverse rendering tasks, possibly through adapting resolution and further optimizing the implicit-to-explicit representation. Additionally, the authors suggest the exploration of incorporating ray tracing techniques to address the current model's limitations in representing higher-order lighting effects, especially shadows and inter-reflections.

In conclusion, GeoSplatting stands out as an efficient and effective method for enhancing inverse rendering performance, with substantial implications for both academic research and practical applications in computer graphics. Its capacity to blend accuracy and efficiency sets a new benchmark in the quest for realistic scene reconstruction and rendering.