AniSDF: Fused-Granularity Neural Surfaces with Anisotropic Encoding for High-Fidelity 3D Reconstruction (2410.01202v2)

Abstract: Neural radiance fields have recently revolutionized novel-view synthesis and achieved high-fidelity renderings. However, these methods sacrifice the geometry for the rendering quality, limiting their further applications including relighting and deformation. How to synthesize photo-realistic rendering while reconstructing accurate geometry remains an unsolved problem. In this work, we present AniSDF, a novel approach that learns fused-granularity neural surfaces with physics-based encoding for high-fidelity 3D reconstruction. Different from previous neural surfaces, our fused-granularity geometry structure balances the overall structures and fine geometric details, producing accurate geometry reconstruction. To disambiguate geometry from reflective appearance, we introduce blended radiance fields to model diffuse and specularity following the anisotropic spherical Gaussian encoding, a physics-based rendering pipeline. With these designs, AniSDF can reconstruct objects with complex structures and produce high-quality renderings. Furthermore, our method is a unified model that does not require complex hyperparameter tuning for specific objects. Extensive experiments demonstrate that our method boosts the quality of SDF-based methods by a great scale in both geometry reconstruction and novel-view synthesis.

• The paper introduces a fused-granularity architecture that simultaneously trains coarse and fine hash grids for detailed 3D geometry.
• It employs anisotropic spherical Gaussian encoding to effectively separate diffuse and specular components for realistic rendering.
• Empirical tests across multiple datasets show improved PSNR and reduced Chamfer Distance, indicating superior reconstruction performance.

AniSDF: Fused-Granularity Neural Surfaces with Anisotropic Encoding for High-Fidelity 3D Reconstruction

The paper introduces AniSDF, an approach designed to improve high-fidelity 3D reconstruction using neural implicit surfaces. Addressing the limitations of existing methods in simultaneously achieving accurate geometry and high-quality rendering, AniSDF proposes a fused-granularity geometry structure and a novel anisotropic encoding.

Key Contributions

1. Fused-Granularity Neural Surfaces: This method introduces parallel sets of coarse and fine hash grids which the model trains simultaneously. This design aims to combine the advantages of both resolution levels, ensuring detailed geometric reconstructions without sacrificing broader structural accuracy.
2. Blended Radiance Fields with ASG Encoding: By incorporating anisotropic spherical Gaussians (ASG) in its rendering pipeline, the model effectively distinguishes diffuse from specular components. This approach leverages physics-based rendering principles to capture reflective and non-reflective appearances efficiently.
3. Unified SDF-based Architecture: The integration of these techniques into a single model framework ensures robust performance across various tasks without necessitating complex hyperparameter adjustments for specific instances.

The novel methodologies suggested by the authors address two core challenges prevalent in previous approaches: the accurate modeling of fine geometric details and the disambiguation of complex appearances, such as reflectivity.

Experimental Validation

The paper includes empirical evaluations on datasets like NeRF Synthetic, DTU, Shiny Blender, and Shelly, demonstrating AniSDF's robust performance. The results highlight its competitive rendering quality and superior geometry reconstruction compared to other contemporary methods.

• PSNR and Chamfer Distance: AniSDF shows notable improvement in PSNR scores across datasets, reflecting its capability to produce clearer and more detailed novel-view synthesis. Additionally, reduced Chamfer Distances indicate its efficacy in surface reconstruction.

Implications and Future Prospects

AniSDF's enhancements in geometric detail and rendering quality hold promise for advancements in several AI-driven applications:

• Computer Graphics and Animation: Its ability to capture fine details can improve the visual realism of digital content.
• Augmented Reality: Enhanced 3D reconstructions can lead to more immersive AR experiences.
• Inverse Rendering and Relighting: AniSDF's accurate geometry serves as a critical precursor for applications requiring material estimation.

Future developments could explore real-time adaptation of AniSDF for practical deployment in interactive applications, possibly leveraging SDF-baking methods for performance optimization. Additionally, expanding its capabilities to handle complex indirect illumination could broaden its applicability in scene understanding.

In conclusion, AniSDF represents a significant stride forward in 3D reconstruction technologies, offering substantial improvements in handling complex visual phenomena while maintaining structural fidelity. This model sets a new standard for neural implicit surfaces and their role in AI-driven visual computing.