发表机构
Faculty of Computing; Harbin Institute of Technology（计算学院; 哈尔滨工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出GDSNet，一种基于连续二维高斯基元叠加和可微喷溅渲染的人群计数网络，通过控制点拟合与端到端训练，在四个基准上超越现有方法。
AI 中文摘要
本文提出了一种新颖的人群计数方法，即高斯密度喷溅网络（GDSNet）。与依赖传统基于网格的密度图且对空间分辨率敏感的方法不同，GDSNet将人群表示为连续二维高斯基元的叠加。我们的方法基于两个关键贡献。首先，我们引入了一种基于控制点的拟合机制来结构化高斯参数的预测。我们设计了一种方法来分配一组定义局部区域的控制点，从这些区域中池化特征以回归每个基元的参数。其次，我们将可微的高斯喷溅框架适配到计数任务中，通过为每个基元参数化几何参数和标量密度质量。这种公式化使得网络能够通过可微渲染密度图的空间匹配进行端到端训练，自然地提供局部密度监督和全局计数优化。在四个标准基准上的广泛评估表明，GDSNet持续优于现有技术水平。
英文摘要
This paper proposes a novel crowd counting approach, the Gaussian Density Splatting Network (GDSNet). Unlike methods that rely on conventional, grid-based density maps and are sensitive to spatial resolution, GDSNet represents a crowd as a superposition of continuous 2D Gaussian primitives. Our approach is built upon two key contributions. First, a control-point-based fitting mechanism is introduced to structure the prediction of Gaussian parameters. A set of control points is adaptively allocated to define local regions, from which features are pooled to regress each primitive's parameters. Second, we adapt a differentiable Gaussian Splatting framework to the counting task by parameterizing each primitive with geometric parameters and a scalar density mass. This formulation allows the network to be trained end-to-end via spatial matching of differentiably rendered density maps, naturally providing both local density supervision and global count optimization. Extensive evaluations on four standard benchmarks show GDSNet consistently outperforms the state of the art. The code is available at https://github.com/infinite0522/GDSNet-Gaussian-Density-Splatting-Network.
CommentsThis is the preprint version of the paper and supplemental material to appear in NeurIPS, 2026. Please cite the final published version
