ELGD-GS: Error-Guided Localization and Gradient Consistency-Aware Density Control for Efficient Dynamic Scene Reconstruction

4D Gaussian Splatting (4DGS) delivers high-quality dynamic scene reconstruction with real-time rendering. However, existing 4DGS methods adopt densification strategies that allocate Gaussians inefficiently. They over-populate well-reconstructed regions and ignore the influence of gradient consistency, leading to unnecessary storage overhead and suboptimal reconstruction quality. In this paper, we propose ELGD-GS, an Error-guided Localization and Gradient Consistency-aware Density Control of 4D Gaussian Splatting framework that enables high-quality dynamic scene reconstruction with efficient Gaussian representations. First, we introduce an error-guided Gaussian localization mechanism to identify 4D Gaussians requiring densification based on pixel-wise reconstruction errors. By recording pixel-wise photometric residuals and high-frequency structural discrepancies over time, this mechanism effectively prevents redundant Gaussian growth in well-reconstructed regions. Second, we propose a gradient consistency-aware density control strategy that measures gradient direction consistency across timestamps to regulate Gaussian growth. It facilitates the effective splitting of Gaussians affected by conflicting gradients while suppressing unnecessary densification of Gaussians with highly consistent gradient directions, thereby improving structural fidelity and representation efficiency. Experiments across multiple datasets demonstrate that ELGD-GS achieves substantial improvements in reconstruction quality and highly compact representations, outperforming the state-of-the-art method TAD-GS by a 0.42 dB PSNR gain and a 58% storage reduction on the N3DV dataset. The source code will be released at http://github.com.