AbsGS: Recovering Fine Details for 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2404.10484v1
• Date: Tue, 16 Apr 2024 11:44:12 GMT
• Title: AbsGS: Recovering Fine Details for 3D Gaussian Splatting
• Authors: Zongxin Ye, Wenyu Li, Sidun Liu, Peng Qiao, Yong Dou,
• Abstract summary: 3D Gaussian Splatting (3D-GS) technique couples 3D primitives with differentiable Gaussianization to achieve high-quality novel view results. However, 3D-GS frequently suffers from over-reconstruction issue in intricate scenes containing high-frequency details, leading to blurry rendered images. We present a comprehensive analysis of the cause of aforementioned artifacts, namely gradient collision. Our strategy efficiently identifies large Gaussians in over-reconstructed regions, and recovers fine details by splitting.
• Score: 10.458776364195796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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: https://ty424.github.io/AbsGS.github.io/

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