用JPEG压缩参数指导3D高斯点云渲染，提升低质图像下的重建质量。
JSGS: JPEG State-Guided Supervision for 3D Gaussian Splatting from Mixed-Quality Views
- 基于JPEG量化表构建视图专属观测算子，实现跨视图一致性监督。
- 在7个场景、3种画质混合策略下均达到最低LPIPS和最高SSIM。
- 适合处理含压缩伪影的多质量图像，实时渲染约150 FPS。
标准3D高斯点云（3DGS）假设输入图像准确采样场景辐射率，但混合质量的JPEG图像会因压缩导致的块效应和振铃伪影，破坏跨视图共享高斯体的更新。为此，我们提出针对混合质量视图的JPEG状态引导监督（JSGS）。JSGS利用每张JPEG文件中存储的亮度和色度量化表，构建视图特定的JPEG观测算子，对每幅合成视图进行编码-解码，实现与对应解码输入图像的域匹配比较。亮度量化表在固定中频段提供连续权重，低频损失锚定粗结构，加权中频损失则在选定的DCT坐标间重新分配监督信号。由此产生的块不一致还引导高斯控制器对高透明度区域的小原型进行正则化。在七个场景和三种混合质量方案下，JSGS在所有条件下均取得最低平均LPIPS和最高平均SSIM，同时保持约150 FPS的渲染速度。
原文摘要 · Abstract (English)
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.
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