Real-time Large-scale Deformation of Gaussian Splatting

IF 7.8 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Lin Gao, Jie Yang, Bo-Tao Zhang, Jia-Mu Sun, Yu-Jie Yuan, Hongbo Fu, Yu-Kun Lai
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引用次数: 0

Abstract

Neural implicit representations, including Neural Distance Fields and Neural Radiance Fields, have demonstrated significant capabilities for reconstructing surfaces with complicated geometry and topology, and generating novel views of a scene. Nevertheless, it is challenging for users to directly deform or manipulate these implicit representations with large deformations in a real-time fashion. Gaussian Splatting (GS) has recently become a promising method with explicit geometry for representing static scenes and facilitating high-quality and real-time synthesis of novel views. However, it cannot be easily deformed due to the use of discrete Gaussians and the lack of explicit topology. To address this, we develop a novel GS-based method (GaussianMesh) that enables interactive deformation. Our key idea is to design an innovative mesh-based GS representation, which is integrated into Gaussian learning and manipulation. 3D Gaussians are defined over an explicit mesh, and they are bound with each other: the rendering of 3D Gaussians guides the mesh face split for adaptive refinement, and the mesh face split directs the splitting of 3D Gaussians. Moreover, the explicit mesh constraints help regularize the Gaussian distribution, suppressing poor-quality Gaussians ( e.g. , misaligned Gaussians, long-narrow shaped Gaussians), thus enhancing visual quality and reducing artifacts during deformation. Based on this representation, we further introduce a large-scale Gaussian deformation technique to enable deformable GS, which alters the parameters of 3D Gaussians according to the manipulation of the associated mesh. Our method benefits from existing mesh deformation datasets for more realistic data-driven Gaussian deformation. Extensive experiments show that our approach achieves high-quality reconstruction and effective deformation, while maintaining the promising rendering results at a high frame rate (65 FPS on average on a single commodity GPU).
高斯拼接的实时大尺度变形
神经隐式表示(包括神经距离场和神经辐射场)在重建具有复杂几何形状和拓扑结构的表面以及生成场景的新视图方面已显示出强大的能力。然而,对于用户来说,直接对这些隐式表示进行大变形的实时变形或操作是一项挑战。最近,高斯拼接法(GS)已成为一种很有前途的方法,它具有显式几何结构,可用于表示静态场景,并促进高质量和实时合成新视图。然而,由于使用离散高斯和缺乏明确的拓扑结构,这种方法不能轻易变形。为了解决这个问题,我们开发了一种基于高斯的新方法(GaussianMesh),可以实现交互式变形。我们的主要想法是设计一种创新的基于网格的高斯表示法,并将其集成到高斯学习和操作中。三维高斯是在显式网格上定义的,它们之间相互绑定:三维高斯的渲染引导网格面的分割以进行自适应细化,网格面的分割引导三维高斯的分割。此外,明确的网格约束有助于规范高斯分布,抑制劣质高斯(如错位高斯、长窄形高斯),从而提高视觉质量,减少变形时的伪影。在此表示法的基础上,我们进一步引入了大规模高斯变形技术来实现可变形高斯,该技术可根据对相关网格的操作来改变三维高斯的参数。我们的方法得益于现有的网格变形数据集,可实现更逼真的数据驱动高斯变形。广泛的实验表明,我们的方法实现了高质量的重建和有效的变形,同时以较高的帧频(在单个商用 GPU 上平均为 65 FPS)保持了良好的渲染效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Transactions on Graphics
ACM Transactions on Graphics 工程技术-计算机:软件工程
CiteScore
14.30
自引率
25.80%
发文量
193
审稿时长
12 months
期刊介绍: ACM Transactions on Graphics (TOG) is a peer-reviewed scientific journal that aims to disseminate the latest findings of note in the field of computer graphics. It has been published since 1982 by the Association for Computing Machinery. Starting in 2003, all papers accepted for presentation at the annual SIGGRAPH conference are printed in a special summer issue of the journal.
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