Fix False Transparency by Noise Guided Splatting
Aly Hakie, Yiren Lu, Yu Yin, Michael Jenkins, Yehe Liu
3D Gaussian Splatting (3DGS) has demonstrated impressive capabilities in 3D reconstruction. However, its α-blending can induce ’false transparency’ artifacts, particularly where low point cloud density in sparse or low-texture regions causes foreground objects to appear improperly transparent. This issue stems from an ill-posed optimization. During training, background Gaussians blend with foreground ones, making them difficult to differentiate using only photometric loss, which leads to the observed transparency in these regions. This view-inconsistency issue is hard to detect in static renderings during training and validation, but becomes evident in object-centric reconstruction during interactive rotation. Although other causes of view-inconsistency (e.g., popping artifacts) have been explored recently, false transparency has not been explicitly identified. This paper proposes a novel explanation to the problem and a solution to remedy it by injecting opaque noise Gaussians in the object volume during training. Our strategy, Noise Guided Splatting ( NGS), encourages surface Gaussians to adopt higher opacity while minimally modifying the existing splatting process. To quantitatively evaluate the false transparency in static renderings, we propose a transmittance-based metric to characterize the extent of the false transparency problem. We also introduce a customized high-quality object-centric scan dataset exhibiting prominent transparency issues and supplement popular existing datasets (e.g., DTU) with new complementary infill noise specifically designed to evaluate false transparency handling in 3D reconstruction methods. Across various datasets, NGS substantially reduces surface transmittance while maintaining performance on standard rendering metrics (e.g., PSNR), demonstrating its effectiveness