Of course! And this was done many times in the past, probably with better results than current deep learning based gaussian splatting where they use way too many splats to render a scene.
Basically the problem with sparse pictures and point clouds in general is their lack of topology and not precise spatial position. But when you already have the topology (eg with a mesh), you can extract (optimally) a set of points and compute the radius of the splats such that there are no holes in the final image (and their color). That is usually done with the curvature and the normal.
The 'optimally' part is difficult, an easier and faster approach is just to do a greedy pass to select good enough splats.
Basically the problem with sparse pictures and point clouds in general is their lack of topology and not precise spatial position. But when you already have the topology (eg with a mesh), you can extract (optimally) a set of points and compute the radius of the splats such that there are no holes in the final image (and their color). That is usually done with the curvature and the normal.
The 'optimally' part is difficult, an easier and faster approach is just to do a greedy pass to select good enough splats.