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Gaussian Frosting: Editable Complex Radiance Fields with Real-Time Rendering (2403.14554v1)

Published 21 Mar 2024 in cs.CV and cs.GR

Abstract: We propose Gaussian Frosting, a novel mesh-based representation for high-quality rendering and editing of complex 3D effects in real-time. Our approach builds on the recent 3D Gaussian Splatting framework, which optimizes a set of 3D Gaussians to approximate a radiance field from images. We propose first extracting a base mesh from Gaussians during optimization, then building and refining an adaptive layer of Gaussians with a variable thickness around the mesh to better capture the fine details and volumetric effects near the surface, such as hair or grass. We call this layer Gaussian Frosting, as it resembles a coating of frosting on a cake. The fuzzier the material, the thicker the frosting. We also introduce a parameterization of the Gaussians to enforce them to stay inside the frosting layer and automatically adjust their parameters when deforming, rescaling, editing or animating the mesh. Our representation allows for efficient rendering using Gaussian splatting, as well as editing and animation by modifying the base mesh. We demonstrate the effectiveness of our method on various synthetic and real scenes, and show that it outperforms existing surface-based approaches. We will release our code and a web-based viewer as additional contributions. Our project page is the following: https://anttwo.github.io/frosting/

Citations (3)

Summary

  • The paper presents an innovative method that integrates mesh extraction with adaptive 3D Gaussian Frosting for real-time editable radiance field rendering.
  • It leverages a refined SuGaR regularization technique to extract base meshes and applies variable Gaussian layers to capture fine details and volumetric effects.
  • The approach balances computational efficiency with rendering quality, opening new avenues for dynamic 3D scene manipulation and simulation of complex materials.

Gaussian Frosting: Enhancing Complex Radiance Field Manipulation and Real-time Rendering

Surface and Volume: A Convergence for Editable Radiance Fields

In the continuous journey to bridge the gap between high-quality rendering and the need for editable 3D representations, Gaussian Frosting emerges as a compelling proposition. This novel methodology enriches mesh-based scene representations through an adaptive layer of 3D Gaussians, termed as "Gaussian Frosting." This hybrid approach not only facilitates the editing and animation of complex 3D effects in real-time but also retains the rendering merits typical of volumetric methods. Herein, we delve into the makeup of Gaussian Frosting, exploring its foundational concepts, the inherent advantages, challenges it addresses, and the potential it harbors for future developments in the field of 3D scene manipulation and rendering.

The Blend of Surface and Volumetric Representations

The Conceptual Framework

Gaussian Frosting is built upon the 3D Gaussian Splatting (3DGS) framework, extending its capabilities to better capture the intricacies of both flat and fuzzy materials. At its core, Gaussian Frosting operates by first extracting a base mesh from an optimized set of 3D Gaussians. It then enhances this base mesh with a surrounding layer of adaptively placed Gaussians, the density and distribution of which are meticulously adjusted to encapsulate fine details and volumetric nuances such as hair or foliage.

This dual representation unifies the editability and efficiency of mesh-based models with the rendering quality of volumetric techniques. The practicality of this approach is evident in its application, where complex scenes, encapsulating everything from the subtleties of fur to the precision of flat surfaces, are rendered with remarkable fidelity and flexibility.

Key Advancements and Methodology

Surface Extraction and Frosting Layer Optimization

The journey from a collection of 3D Gaussians to a refined editable model is meticulously crafted. The process commences with the extraction of a base mesh through a refined version of SuGaR's regularization technique. Following this, an adaptive Gaussian Frosting layer is constructed around the mesh, its thickness varying based on the local material properties deduced from the initial unregularized Gaussians.

This adaptive strategy is crucial for allocating computational resources where they are most needed, thus ensuring a judicious balance between rendering quality and computational efficiency. Moreover, a novel parametrization scheme for Gaussians within the Frosting layer ensures their coherent manipulation during mesh editing or animation, significantly enhancing the model's versatility.

Implications and Future Directions

Bridging Theory and Practice

Gaussian Frosting addresses several perennial challenges in the landscape of 3D rendering and editing. It provides a tangible solution to the dichotomy between the need for high-quality, volumetric rendering and the practical requirements of editability and animation. The resultant models exhibit a depth of detail and realism, hitherto challenging to achieve with purely surface-based or volumetric methods alone.

Speculations on the Horizon

The implications of Gaussian Frosting extend beyond the immediate advancements in rendering and model manipulation. It proposes a framework wherein the computational expenditure aligns more closely with visual and editing demands, paving the way for more efficient and scalable solutions in real-time 3D applications. Moreover, the inherent flexibility of the Gaussian Frosting layer in encapsulating complex geometries and materials hints at broader applications in simulating and rendering intricate physical phenomena and tactile materials.

Concluding Observations

Gaussian Frosting represents a significant stride toward reconciling the high fidelity of volumetric rendering with the pragmatic necessities of 3D model editing and animation. By intelligently merging surface and volumetric representations, it opens new avenues for creating, manipulating, and visualizing 3D scenes in real-time. As this research progresses, it holds the promise of transforming the paradigms of 3D rendering, making the creation of dynamic, complex, and lifelike 3D environments more accessible and efficient.

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