FaithFusion：通过像素级信息增益和谐融合重建与生成
FaithFusion: Harmonizing Reconstruction and Generation via Pixel-wise Information Gain
AI总结：
FaithFusion通过像素级信息增益融合3DGS与扩散模型，实现高保真的驾驶场景生成。
摘要：
在可控驾驶场景重建和3D场景生成中，保持几何保真度的同时，在大视角变化下合成视觉合理的外观至关重要。然而，有效融合基于几何的3DGS和基于外观的扩散模型面临固有挑战，因为缺乏像素级、3D一致的编辑标准往往导致过度修复和几何漂移。为了解决这些问题，我们引入了FaithFusion，一种由像素级预期信息增益（EIG）驱动的3DGS-扩散融合框架。EIG作为统一的策略，用于协调时空合成：它指导扩散作为空间先验来细化高不确定性区域，而其像素级加权将编辑回炼入3DGS。所得到的即插即用系统无需额外的先验条件和结构修改。在Waymo数据集上的广泛实验表明，我们的方法在NTA-IoU、NTL-IoU和FID上均取得SOTA性能，即使在6米车道位移下仍保持FID为107.47。我们的代码可在https://github.com/wangyuanbiubiubiu/FaithFusion上获取。
英文摘要：
In controllable driving-scene reconstruction and 3D scene generation, maintaining geometric fidelity while synthesizing visually plausible appearance under large viewpoint shifts is crucial. However, effective fusion of geometry-based 3DGS and appearance-driven diffusion models faces inherent challenges, as the absence of pixel-wise, 3D-consistent editing criteria often leads to over-restoration and geometric drift. To address these issues, we introduce \textbf{FaithFusion}, a 3DGS-diffusion fusion framework driven by pixel-wise Expected Information Gain (EIG). EIG acts as a unified policy for coherent spatio-temporal synthesis: it guides diffusion as a spatial prior to refine high-uncertainty regions, while its pixel-level weighting distills the edits back into 3DGS. The resulting plug-and-play system is free from extra prior conditions and structural modifications.Extensive experiments on the Waymo dataset demonstrate that our approach attains SOTA performance across NTA-IoU, NTL-IoU, and FID, maintaining an FID of 107.47 even at 6 meters lane shift. Our code is available at https://github.com/wangyuanbiubiubiu/FaithFusion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
