Abstract
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: this https URL
Abstract (translated)
3D高斯平铺（3D-GS）技术将3D高斯基本体与有条件的栅格化渲染相结合以实现高质量的新视图合成结果，同时提供先进的实时渲染性能。然而，由于其在3D-GS中的自适应密度控制策略的缺陷，它经常在包含高频细节的复杂场景中陷入过度重建问题，导致模糊渲染图像。导致缺陷的根本原因至今仍未被深入探讨。在这项工作中，我们全面分析了上述伪影的原因，即梯度碰撞，它阻止了在重构区域中大型高斯体的分裂。为了应对这个问题，我们提出了一个新的同向维度位置梯度作为密度化的标准。我们的策略有效地在重构区域中的大型高斯体，并通过分裂来恢复细节。我们对所提出的方法在各种具有挑战性的数据集上进行了评估。实验结果表明，我们的方法在降低或类似内存消耗的情况下实现了最佳的渲染质量。我们的方法易于实现，可以集成到各种基于高斯平铺的最近方法中。我们将在正式发表后开源我们的代码。我们的项目页面可用于此链接：https://this URL
URL
https://arxiv.org/abs/2404.10484
