3D Gaussian Splatting reconstructs scenes by starting from a sparse Structure-from-Motion initialization and refining under-reconstructed regions. This process is slow, as it requires multiple densification steps where Gaussians are repeatedly split and adjusted, following a lengthy optimization path. Moreover, this incremental approach often yields suboptimal renderings in high-frequency regions. We propose a fundamentally different approach: eliminate densification with a one-step approximation of scene geometry using triangulated pixels from dense image correspondences. This dense initialization allows us to estimate the rough geometry of the scene while preserving rich details from input RGB images, providing each Gaussian with well-informed color, scale, and position. As a result, we dramatically shorten the optimization path and remove the need for densification. Unlike methods that rely on sparse keypoints, our dense initialization ensures uniform detail across the scene, even in high-frequency regions where other methods struggle. Moreover, since all splats are initialized in parallel at the start of optimization, we remove the need to wait for densification to adjust new Gaussians. EDGS reaches LPIPS and SSIM performance of standard 3DGS significantly faster than existing efficiency-focused approaches. When trained further, it exceeds the reconstruction quality of state-of-the-art models aimed at maximizing fidelity. Our method is fully compatible with other acceleration techniques, making it a versatile and efficient solution that can be integrated with existing approaches.

翻译：三维高斯溅射通过从稀疏的运动结构初始化出发，并优化欠重建区域来完成场景重建。这一过程速度缓慢，因为它需要多次致密化步骤，即高斯函数需反复分裂和调整，遵循冗长的优化路径。此外，这种渐进式方法在高频区域往往产生次优的渲染结果。我们提出了一种根本不同的方法：利用密集图像对应关系中的三角化像素对场景几何进行一步近似，从而消除致密化步骤。这种密集初始化使我们能够估计场景的粗略几何结构，同时保留输入RGB图像中的丰富细节，为每个高斯函数提供信息充分的颜色、尺度和位置。因此，我们显著缩短了优化路径，并消除了对致密化的需求。与依赖稀疏关键点的方法不同，我们的密集初始化确保了整个场景细节的均匀性，即使在其它方法难以处理的高频区域也是如此。此外，由于所有溅射点均在优化开始时并行初始化，我们无需等待致密化步骤来调整新的高斯函数。EDGS在达到标准3DGS的LPIPS和SSIM性能指标方面，显著快于现有的注重效率的方法。当进一步训练时，其重建质量超过了旨在最大化保真度的最先进模型。我们的方法与其它加速技术完全兼容，使其成为一种可集成到现有方法中的通用高效解决方案。