JSGS: JPEG State-Guided Supervision for 3D Gaussian Splatting from Mixed-Quality Views

2026-08-09

AI Summary

This work addresses the limitation of standard 3D Gaussian splatting, which assumes pristine input images and struggles with multi-view datasets containing mixed-quality imagery affected by JPEG compression artifacts. To overcome this, the authors propose the first method that explicitly incorporates JPEG compression information into the training pipeline by leveraging quantization tables to construct view-specific observation operators, enabling consistent supervision directly in the compressed domain. They further introduce a DCT-band-weighted loss and a Gaussian regularization strategy based on block-wise inconsistency to guide the optimization toward more robust scene representations. Evaluated across seven scenes under three mixed-quality configurations, the proposed approach consistently achieves the lowest average LPIPS and highest average SSIM while maintaining a rendering speed of approximately 150 FPS.

Abstract

Standard 3D Gaussian Splatting (3DGS) assumes that every input image faithfully samples scene radiance. However, mixed-quality JPEG images violate this assumption because compression-induced blocking and ringing artifacts can corrupt updates to Gaussians shared across views. To address this problem, we propose JPEG State-Guided Supervision for 3D Gaussian Splatting from Mixed-Quality Views (JSGS). JSGS uses luminance and chrominance quantization tables stored in each JPEG file to construct a view-specific JPEG observation operator. This operator encodes and decodes each rendered view for domain-matched comparison with the corresponding decoded input image. The luminance quantization table supplies continuous weights within a fixed middle frequency band. A loss in the low frequency band anchors coarse structure, while the weighted middle frequency loss redistributes supervision among the selected DCT coordinates. The resulting block disagreement also guides the Gaussian Controller to regularize small primitives with high opacity in disagreement regions. Across seven scenes and three mixed-quality schedules, JSGS achieves the lowest mean LPIPS and the highest mean SSIM under every schedule while rendering at approximately 150 FPS. Code: https://github.com/Jayden-Cui/JSGS.

Problem

• 3D Gaussian Splatting
• JPEG compression artifacts
• mixed-quality views
• radiance reconstruction
• compression-induced distortion

Innovation

• JPEG-aware rendering
• 3D Gaussian Splatting
• quantization table guidance
• frequency-domain supervision
• mixed-quality view reconstruction