SharpGS: Sharpness-Preserving 3D Gaussian Splatting with Differentiable Blur-Driven Density Control

We present SharpGS, a differentiable blur-driven density con-trol, which enhances the quality of 3D Gaussian Splatting (3DGS). Stan-dard 3DGS often struggles to capture intricate details, particularly intextured patterns and object boundaries, due to the limited sensitivityof its density control. Simple finer densification can result in excessiveprimitive counts with marginal quality gain. To address this, we intro-duce blur as an effective perceptual cue, leveraging CUDA-based differ-entiable blur. Our density control estimates per-primitive blur levels bycomparing reconstructions to blurred ground-truth images, and identifieshigh-frequency regions where additional primitives are required. Whilethis naturally produces more primitives, we counterbalance this by sup-pressing redundant primitives. Specifically, we penalize the blur levelsof inherently smooth regions such as sky, and regularize the opacitiesof potential split/clone candidates. We experimentally demonstrate thatSharpGS greatly improves the state-of-the-art 3DGS methods in termsof quality, while keeping learned primitives compact.