MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and Photorealism From Sparse Views (2412.06767v1)

Abstract: We present a novel appearance model that simultaneously realizes explicit high-quality 3D surface mesh recovery and photorealistic novel view synthesis from sparse view samples. Our key idea is to model the underlying scene geometry Mesh as an Atlas of Charts which we render with 2D Gaussian surfels (MAtCha Gaussians). MAtCha distills high-frequency scene surface details from an off-the-shelf monocular depth estimator and refines it through Gaussian surfel rendering. The Gaussian surfels are attached to the charts on the fly, satisfying photorealism of neural volumetric rendering and crisp geometry of a mesh model, i.e., two seemingly contradicting goals in a single model. At the core of MAtCha lies a novel neural deformation model and a structure loss that preserve the fine surface details distilled from learned monocular depths while addressing their fundamental scale ambiguities. Results of extensive experimental validation demonstrate MAtCha's state-of-the-art quality of surface reconstruction and photorealism on-par with top contenders but with dramatic reduction in the number of input views and computational time. We believe MAtCha will serve as a foundational tool for any visual application in vision, graphics, and robotics that require explicit geometry in addition to photorealism. Our project page is the following: https://anttwo.github.io/matcha/

• The paper presents a novel atlas-based method that uses 2D Gaussian surfels for precise 3D geometry reconstruction and photorealistic rendering from limited views.
• It integrates pretrained monocular depth estimation with a neural deformation model operating in 2D to efficiently refine surface details.
• Benchmarked on datasets like DTU and Tanks and Temples, the approach achieves superior Chamfer Distance metrics, promising advances in AR, robotics, and digital content creation.

Analysis of "MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and Photorealism From Sparse Views"

The paper "MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and Photorealism From Sparse Views" introduces a novel method for reconstructing high-fidelity 3D surface meshes from sparse view images with photorealistic rendering capabilities. This work presents a significant step in resolving the issues of high-quality surface recovery and rendering in sparse-view settings, an area that has remained challenging in the domain of computer vision and graphics.

Methodology

The authors propose MAtCha Gaussians, a model that interprets surfaces as an Atlas of Charts rendered via 2D Gaussian surfels, aligning with the goals of maintaining photorealistic novel view synthesis and high-precision geometric reconstruction. The initial alignment leverages a pretrained monocular depth estimation, providing a detailed and high-frequency initial geometry. The model evolves through the integration of (1) a neural deformation model that optimizes surfaces in 2D rather than 3D space, thus ensuring computational efficiency, and (2) a differentiable rendering process using Gaussian splatting to refine the geometry.

A unique aspect of the methodology is how the authors handle scale ambiguities inherent to monocular depth estimation. Through sparse-view structure from motion (SfM) data, the authors enforce alignment between the charts using fitting, structure, and mutual alignment losses. Notably, the deformation model includes depth-dependent encodings which augment positional precision without introducing excessive complexity, making it robust against sparse inputs.

Numerical Results

The paper meticulously validates the proposed method against established benchmarks such as the DTU dataset and Tanks and Temples benchmark. Both qualitative and quantitative results reflect a substantial enhancement over existing methods like SparseNeus and GOF augmented with MASt3R-SfM, especially under extreme sparsity where the view count is limited to 3-10 images. The method achieves superior Chamfer Distance metrics and rendering quality compared to prior state-of-the-art approaches. Such performance is confirmed across a variety of scenes spanning bounded and unbounded environments.

Implications and Future Developments

MAtCha Gaussians present a compelling advancement, particularly for practical applications in robotics, digital content creation, and augmented reality, where accurate 3D reconstruction from limited visual data is crucial. The potential for real-time applications also arises due to the method's computational efficiency and reduced input requirements.

The theoretical underpinnings suggest that focusing on a robust deformable model with sparse view optimization can significantly complement high-frequency geometry preservation—a paradigm that may inspire future research towards lightweight yet highly accurate geometric representations in other domains, such as dynamic scene reconstruction and semantic-aware rendering.

Future research may build upon this foundation by exploring extensions to dynamic view scenes, integration into broader neural rendering pipelines with dynamic lighting conditions, or coupling with semantic segmentation networks for enhanced scene understanding and manipulation. Considerations might also focus on enhancing the generalizable capabilities across more diverse datasets and application scenarios, further demonstrating the versatility of chart-based 3D representations.

Overall, "MAtCha Gaussians" is poised to influence the trajectory of image-based 3D modeling, sustaining a harmonious blend of computational rigor and innovative rendering approaches, potentially serving as a catalyst for future work across related interdisciplinary domains.