# CF-Splat：带网格先验的闭式三角形 Splatting

> 原文链接：https://robotics.hellobike.com/papers/2026-10-15-2610-22108
> 频道：papers | 更新日期：2026-10-15 | 标签：arxiv, cs.RO
> 核心摘要：CF-Splat在已知网格和标定相机上将外观估计化为线性最小二乘，并在多种真实数据集验证高效NVS。

# CF-Splat：带网格先验的闭式三角形 Splatting

::: v-pre
> 原文链接：[CF-Splat: Closed-Form Triangle Splatting with Mesh Priors](https://arxiv.org/abs/2610.22108)（arXiv 2610.22108 · 发布日期：2026-10-15）

**CF-Splat: Closed-Form Triangle Splatting with Mesh Priors**

CF-Splat在已知网格和标定相机上将外观估计化为线性最小二乘，并在多种真实数据集验证高效NVS。

## 核心要点

- 现有 splatting 方法通常需迭代非线性优化大量耦合参数，计算时间和显存开销较高且易陷局部最小。
- 方法利用给定网格几何与标定相机，将像素颜色表示为顶点球谐系数的线性函数，求解全局线性最小二乘。
- 引入自适应球谐正则约束观测不足的高阶系数，并采用矩阵无关预条件共轭梯度求解器扩展到大场景。
- 在多种真实世界数据集上，NVS质量达到有竞争力或更优，同时较3DGS显著减少计算时间并降低平均峰值GPU显存。

**验证范围与局限**：验证范围为多种真实世界数据集上的已知网格场景，网格可预先存在或由上游建图/重建流程提供。

## 英文摘要 (Abstract)

Splatting-based radiance-field representations have recently gained popularity in robotic perception, enabling high-quality novel-view synthesis (NVS) through explicit scene primitives. However, existing splatting methods typically rely on iterative nonlinear optimization that jointly optimizes millions of coupled primitive parameters, resulting in substantial computation time, peak GPU memory consumption, and susceptibility to local minima. In this paper, we propose CF-Splat, a novel closed-form triangle-splatting framework for efficient scene appearance estimation over a known mesh either pre-existing or provided by upstream mapping or reconstruction pipelines. Our key insight is that, by properly formulating the problem over the given mesh geometry and calibrated cameras, the rendered pixel colors can be expressed linearly with respect to the per-vertex spherical harmonics (SH) coefficients, allowing multi-view scene appearance estimation to be formulated as a global linear least-squares (LLS) problem without the need of iterative nonlinear optimization. To guarantee a well-posed solution under uneven multi-view observation coverage, we introduce adaptive SH regularization that constrains insufficiently supported high-order SH coefficients and yields a unique global minimizer. To ensure the scalability to large-scale scenes, we develop a triangle-structured matrix-free preconditioned conjugate-gradient solver that avoids explicitly constructing the large sparse normal system while producing the same LLS solution. Experiments on diverse real-world datasets demonstrate that CF-Splat achieves competitive or superior NVS quality compared with state-of-the-art splatting methods. While, CF-Splat requires approximately 27.5-378.0 times less computation time and 1.8 times lower peak GPU memory on average than 3DGS across scenes of different scales on a consumer-grade laptop.

作者：Yixin Fang, Liang Zhao
:::

## 原始参考来源 (Sources)

- [arXiv 2610.22108：CF-Splat: Closed-Form Triangle Splatting with Mesh Priors](https://arxiv.org/abs/2610.22108)