Mani-GS：基于三角网格的高斯泼溅操控方法
Mani-GS: Gaussian Splatting Manipulation with Triangular Mesh
AI总结：
针对3D高斯泼溅（3DGS）缺乏有效操控手段的问题，提出基于三角网格的自适应直接操控方法Mani-GS，可在保持高保真渲染的同时实现多种操控操作，对不精确网格也有效。
摘要：
神经辐射场（NeRF）等神经3D表示方法擅长生成照片级真实感的渲染结果，但缺乏内容创作所需的关键操控与编辑灵活性。已有研究尝试通过在规范空间中变形NeRF，或基于显式网格操控辐射场来解决这一问题，但NeRF的操控可控性不足，且训练与推理耗时较长。随着3D高斯泼溅（3DGS）的出现，基于显式点式3D表示的方法可实现极高保真度的新视角合成，训练与渲染速度也大幅提升，但目前仍缺乏在保持渲染质量的前提下自由操控3DGS的有效手段。本研究旨在解决可操控的照片级真实感渲染这一难题，提出利用三角网格对3DGS进行自适应直接操控，该方法无需为不同类型的高斯操控设计各类算法。通过采用具备三角形形状感知的高斯绑定与适配方法，我们可实现3DGS操控，且操控后仍能保持高保真渲染效果。我们的方法可处理大形变、局部操控以及软体模拟，同时维持高质量渲染。此外，我们证明该方法对从3DGS中提取的不精确网格同样有效。实验结果验证了方法的有效性，且其性能优于基线方法。
英文摘要：
Neural 3D representations such as Neural Radiance Fields (NeRF), excel at producing photo-realistic rendering results but lack the flexibility for manipulation and editing which is crucial for content creation. Previous works have attempted to address this issue by deforming a NeRF in canonical space or manipulating the radiance field based on an explicit mesh. However, manipulating NeRF is not highly controllable and requires a long training and inference time. With the emergence of 3D Gaussian Splatting (3DGS), extremely high-fidelity novel view synthesis can be achieved using an explicit point-based 3D representation with much faster training and rendering speed. However, there is still a lack of effective means to manipulate 3DGS freely while maintaining rendering quality. In this work, we aim to tackle the challenge of achieving manipulable photo-realistic rendering. We propose to utilize a triangular mesh to manipulate 3DGS directly with self-adaptation. This approach reduces the need to design various algorithms for different types of Gaussian manipulation. By utilizing a triangle shape-aware Gaussian binding and adapting method, we can achieve 3DGS manipulation and preserve high-fidelity rendering after manipulation. Our approach is capable of handling large deformations, local manipulations, and soft body simulations while keeping high-quality rendering. Furthermore, we demonstrate that our method is also effective with inaccurate meshes extracted from 3DGS. Experiments conducted demonstrate the effectiveness of our method and its superiority over baseline approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
