Tri-DehazeGS：场景-介质解耦的高斯泼溅与透射率感知优化

中文摘要 AI 辅助

从有雾的多视角图像中恢复干净的3D场景具有挑战性，因为雾会衰减场景辐射并引入大气散射。最近的散射感知高斯泼溅方法将物理雾模型引入重建中，但它们通常在图像空间中应用退化或将介质相关变量绑定到高斯原语上，这可能会将干净的场景辐射与大气效应纠缠在一起。此外，低透射率区域为高斯优化提供了弱化的监督，导致远处或浓雾区域重建不足。我们认为，雾下的干净重建需要场景-介质解耦和透射率感知的优化再平衡。为此，我们提出了Tri-DehazeGS，一个场景-介质解耦的高斯泼溅框架。它用高斯原语表示干净场景，使用独立的视图共享三平面场建模参与介质，并通过物理散射模型合成有雾观测。我们进一步引入了介质解耦透射率梯度补偿（MD-TGC），该补偿在介质冻结后补偿雾抑制的梯度，而不改变前向渲染。在真实和合成雾基准上的实验表明，Tri-DehazeGS提高了干净的新视角重建质量。代码可在以下网址获取：此https URL。

英文摘要

Recovering clean 3D scenes from hazy multi-view images is challenging because haze attenuates scene radiance and introduces atmospheric scattering. Recent scattering-aware Gaussian Splatting methods introduce physical haze models into reconstruction, but they often apply degradation in image space or bind medium-related variables to Gaussian primitives, which can entangle clean scene radiance with atmospheric effects. Moreover, low-transmittance regions provide weakened supervision for Gaussian optimization, causing distant or dense-haze areas to be under-reconstructed. We argue that clean reconstruction under haze requires both scene--medium disentanglement and transmittance-aware optimization rebalancing. To this end, we propose Tri-DehazeGS, a scene--medium decoupled Gaussian Splatting framework. It represents the clean scene with Gaussian primitives, models the participating medium using an independent view-shared tri-plane field, and composes hazy observations through a physical scattering model. We further introduce Medium-Decoupled Transmittance Gradient Compensation (MD-TGC), which compensates haze-suppressed gradients after medium freezing without altering forward rendering. Experiments on real and synthetic haze benchmarks show that Tri-DehazeGS improves clean novel-view reconstruction. Code is available at https://github.com/aptx46/Tri-DehazeGS.

发表机构

• Harbin Institute of Technology（哈尔滨工业大学）
• Meituan（美团）

机构由 AI 辅助整理，请以论文原文为准。