Fix False Transparency by Noise Guided Splatting

Aly El Hakie, Yiren Lu, Yu Yin, Michael W. Jenkins, Yehe Liu

Keywords: 3D Gaussian Splatting, Computer Graphics

TL;DR: We identify and solve the "false transparency" problem in 3D Gaussian Splatting by injecting opaque noise Gaussians, reducing surface transmittance while maintaining rendering quality.

Abstract:

Opaque objects reconstructed by 3D Gaussian Splatting (3DGS) often exhibit a falsely transparent surface, leading to inconsistent background and internal patterns under camera motion in interactive viewing.
This issue stems from the ill-posed optimization in 3DGS. During training, background and foreground Gaussians are blended via $\alpha$-compositing and optimized solely against the input RGB images using a photometric loss.
As this process lacks an explicit constraint on surface opacity, the optimization may incorrectly assign transparency to opaque regions, resulting in view-inconsistent and falsely transparent output.
This issue is difficult to detect in standard evaluation settings (i.e., rendering static images), but becomes particularly evident in object-centric reconstructions under interactive viewing.
Although other causes of view-inconsistency, such as popping artifacts, have been explored previously, false transparency has not been explicitly identified.
To the best of our knowledge, we are the first to quantify, characterize, and develop solutions for this "false transparency" artifact, an under-reported artifact in 3DGS.
Our strategy, Noise Guided Splatting (NGS), encourages surface Gaussians to adopt higher opacity by injecting opaque noise Gaussians in the object volume during training, requiring only minimal modifications to the existing splatting process.
To quantitatively evaluate false transparency in static renderings, we propose a novel transmittance-based metric that measures the severity of this artifact.
In addition, we introduce a customized, high-quality object-centric scan dataset exhibiting pronounced transparency issues, and we augment popular existing datasets (e.g., DTU) with complementary infill noise specifically designed to assess the robustness of 3D reconstruction methods to false transparency.
Experiments across multiple datasets show that NGS substantially reduces false transparency while maintaining competitive performance on standard rendering metrics (e.g., PSNR), demonstrating its overall effectiveness.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 13581