This paper addresses the limitations of Adaptive Density Control (ADC) in 3D Gaussian Splatting (3DGS), a method for high-quality, photorealistic scene representation. The main contribution is a more principled, pixel-error driven formulation for density control in 3DGS, using an auxiliary per-pixel error function to guide densification. The approach also introduces a mechanism to control the total number of primitives generated per scene and corrects a bias in the current opacity handling strategy during cloning operations. The method leads to consistent quality improvements across various benchmark scenes without sacrificing efficiency. The paper discusses the limitations of the original ADC, including the difficulty in setting gradient magnitude thresholds, scene underfitting in high-texture areas, and the inability to control the number of primitives. The proposed method addresses these issues by using per-pixel error to guide densification, correcting opacity bias after cloning, and controlling primitive growth to prevent out-of-memory errors. The approach is validated on standard benchmark datasets like Mip-NeRF 360, Tanks and Temples, and Deep Blending, showing consistent improvements over baselines including 3DGS and Mip-Splatting. The paper also discusses related works, including improvements in 3DGS such as GS++ and Spec-Gaussian, and the use of Mip-Splatting to reduce aliasing. The proposed method is shown to benefit from these approaches. The paper presents ablation studies and experimental evaluations, demonstrating the effectiveness of the proposed method in improving densification and reducing underfitting in high-texture areas. The results show that the method outperforms baselines in perceptual metrics like LPIPS, particularly in scenes with complex textures. The method is also shown to be effective in controlling the number of primitives and reducing bias in opacity handling. The paper concludes that the proposed approach significantly improves the performance of 3DGS in terms of density control and scene reconstruction.This paper addresses the limitations of Adaptive Density Control (ADC) in 3D Gaussian Splatting (3DGS), a method for high-quality, photorealistic scene representation. The main contribution is a more principled, pixel-error driven formulation for density control in 3DGS, using an auxiliary per-pixel error function to guide densification. The approach also introduces a mechanism to control the total number of primitives generated per scene and corrects a bias in the current opacity handling strategy during cloning operations. The method leads to consistent quality improvements across various benchmark scenes without sacrificing efficiency. The paper discusses the limitations of the original ADC, including the difficulty in setting gradient magnitude thresholds, scene underfitting in high-texture areas, and the inability to control the number of primitives. The proposed method addresses these issues by using per-pixel error to guide densification, correcting opacity bias after cloning, and controlling primitive growth to prevent out-of-memory errors. The approach is validated on standard benchmark datasets like Mip-NeRF 360, Tanks and Temples, and Deep Blending, showing consistent improvements over baselines including 3DGS and Mip-Splatting. The paper also discusses related works, including improvements in 3DGS such as GS++ and Spec-Gaussian, and the use of Mip-Splatting to reduce aliasing. The proposed method is shown to benefit from these approaches. The paper presents ablation studies and experimental evaluations, demonstrating the effectiveness of the proposed method in improving densification and reducing underfitting in high-texture areas. The results show that the method outperforms baselines in perceptual metrics like LPIPS, particularly in scenes with complex textures. The method is also shown to be effective in controlling the number of primitives and reducing bias in opacity handling. The paper concludes that the proposed approach significantly improves the performance of 3DGS in terms of density control and scene reconstruction.