Gaussian Density Splatting Network
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 parameteriz...
| Authors | Miao Shang, Yabin Wang, Xiaopeng Hong |
|---|---|
| Primary category | cs.CV |
| Published | 7 October 2026 |
| Last updated at source | 8 October 2026 |
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