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

本文研究了 3D 高斯喷射技术中的密度控制策略的不足之处，分析了导致过度重构和模糊渲染的原因，并提出了一种新的视图空间渐变方向梯度作为密度增加的判据，有效地解决了该问题，并在各种挑战性数据集上进行了评估，结果显示我们的方法在渲染质量上表现最佳，同时内存消耗降低或相似。