3D Gaussian Splatting (3D-GS) technique couples 3D Gaussian primitives with differentiable rasterization to achieve high-quality novel view synthesis results while providing advanced real-time rendering performance. However, due to the flaw of its adaptive density control strategy in 3D-GS, it frequently suffers from over-reconstruction issue in intricate scenes containing high-frequency details, leading to blurry rendered images. The underlying reason for the flaw has still been under-explored. In this work, we present a comprehensive analysis of the cause of aforementioned artifacts, namely gradient collision, which prevents large Gaussians in over-reconstructed regions from splitting. To address this issue, we propose the novel homodirectional view-space positional gradient as the criterion for densification. Our strategy efficiently identifies large Gaussians in over-reconstructed regions, and recovers fine details by splitting. We evaluate our proposed method on various challenging datasets. The experimental results indicate that our approach achieves the best rendering quality with reduced or similar memory consumption. Our method is easy to implement and can be incorporated into a wide variety of most recent Gaussian Splatting-based methods. We will open source our codes upon formal publication. Our project page is available at: https://ty424.github.io/AbsGS.github.io/

翻译：三维高斯泼溅（3D-GS）技术将三维高斯基元与可微光栅化相结合，在实现高质量新视角合成结果的同时提供先进的实时渲染性能。然而，由于3D-GS自适应密度控制策略的缺陷，该方法在处理包含高频细节的复杂场景时经常出现过度重建问题，导致渲染图像模糊。该缺陷的潜在原因尚未得到充分探讨。本文对上述伪影的成因进行了全面分析，发现其根源在于梯度碰撞——该现象阻碍了过度重建区域中的大型高斯体进行分裂。针对该问题，我们提出将同向视空间位置梯度作为致密化的新判据。所提策略能有效识别过度重建区域中的大型高斯体，并通过分裂恢复精细细节。我们在多个具有挑战性的数据集上评估了该方法。实验结果表明，我们的方法在降低或保持相似内存消耗的同时实现了最佳渲染质量。该方法易于实现，并可集成到最新的大多数基于高斯泼溅的方法中。我们将在正式发表后开源代码。项目页面见：https://ty424.github.io/AbsGS.github.io/