ConstraintGS: Modeling Primitive-Level Reconstruction Constraints for Sparse-View Gaussian Splatting

3D Gaussian Splatting enables high-quality, real-time novel-view synthesis but is prone to overfitting under sparse-view supervision. Beyond the limited number of training views, effective supervision is unevenly distributed across Gaussian primitives: even under the same observations, individual primitives receive substantially different amounts and directional distributions of rendering support. We introduce ConstraintGS, a framework for modeling and exploiting primitive-level reconstruction constraints under sparse views. We analyze how rendering support and viewing geometry jointly characterize constraint strength and examine its relationship with local photometric response. Building on this analysis, we construct a self-opacity-normalized cross-view descriptor that combines support magnitude and directional coverage. The descriptor allocates opacity attenuation under a prescribed mean budget and per-primitive bounds, assigning stronger suppression to weakly constrained primitives. ConstraintGS integrates into existing Gaussian splatting pipelines without modifying their inference procedure. Experiments on three widely used benchmarks demonstrate improved novel-view synthesis quality across multiple Gaussian splatting methods under sparse-view conditions.