MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and Photorealism From Sparse Views
Antoine Guédon, Tomoki Ichikawa, Kohei Yamashita, Ko Nishino
2024-12-10
Summary
This paper talks about MAtCha Gaussians, a new method for creating high-quality 3D models and photorealistic images from just a few photos using advanced techniques in computer graphics.
What's the problem?
Creating detailed 3D models typically requires many images taken from different angles, which can be time-consuming and expensive. Existing methods often struggle to produce sharp details and realistic images when working with a limited number of views, making it hard to generate accurate representations of real-world scenes.
What's the solution?
The authors introduce MAtCha Gaussians, which uses a unique approach to model the geometry of a scene as an 'Atlas of Charts.' This method allows the system to render the scene using 2D Gaussian surfels, which are like tiny paintbrushes that help create the appearance of depth and detail. By combining techniques like monocular depth estimation and a novel neural deformation model, MAtCha can efficiently generate high-quality 3D surfaces and photorealistic images from only a few input photos. This approach is faster and requires fewer images than traditional methods.
Why it matters?
This research is important because it significantly improves how we can create realistic 3D models from limited data, which has applications in fields like virtual reality, gaming, and robotics. By making it easier and quicker to generate high-quality visuals, MAtCha Gaussians can enhance creative projects and technological advancements in various industries.
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/