AbsGS: Recovering Fine Details for 3D Gaussian Splatting
Zongxin Ye Wenyu Li Sidun Liu Peng Qiao Yong Dou
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/