ConFixGS：基于置信度感知扩散先验的学习以修复馈送式3D高斯点溅射在驾驶场景中
ConFixGS: Learning to Fix Feedforward 3D Gaussian Splatting with Confidence-Aware Diffusion Priors in Driving Scenes
- University of California, Los Angeles（加州大学洛杉矶分校）
- University of Cambridge（剑桥大学）
- Technical University of Munich（慕尼黑技术大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出ConFixGS，通过置信度感知扩散先验学习修复馈送式3D高斯点溅射，提升稀疏视角驾驶场景中新型视角合成的鲁棒性，实验显示PSNR提升3.68dB，FID降低近一半。
AI中文摘要：
馈送式3D高斯点溅射（3DGS）在基于轨迹的稀疏视角驾驶场景中常面临挑战。现有高斯修复方法主要针对优化型3DGS，而基于扩散的修复通常局限于观测视角附近的迭代细化，导致馈送式3DGS修复研究不足。本文提出ConFixGS，一种即插即用的方法，通过置信度感知扩散先验学习修复馈送式3DGS。从预训练的馈送式模型出发，ConFixGS生成扩散增强的局部伪目标，并通过基于重投影的交叉检查验证与支撑视角的一致性。生成的密集置信度图指导细化，增强可靠细节并抑制幻觉或不一致证据。在Waymo、nuScenes和KITTI数据集上，ConFixGS提升了挑战性的新型视角合成，PSNR提升达3.68dB，FID降低近一半。结果表明，置信度感知的生成先验与支撑视角一致性融合是稳健馈送式3D驾驶场景重建的关键原理。
英文摘要：
Feedforward 3D Gaussian Splatting (3DGS) often struggles in trajectory-based sparse-view driving scenes. Existing Gaussian repair methods mainly target optimization-based 3DGS, while diffusion-based repair is typically restricted to iterative refinement near observed viewpoints, leaving feedforward 3DGS repair underexplored. We propose ConFixGS, a plug-and-play method that learns to fix feedforward 3DGS with confidence-aware diffusion priors. Starting from a pretrained feedforward model, ConFixGS generates diffusion-enhanced local pseudo-targets and validates them through reprojection-based cross-checking against support views. The resulting dense confidence maps guide refinement, enhancing reliable details while suppressing hallucinated or inconsistent evidence. On Waymo, nuScenes, and KITTI, ConFixGS improves challenging novel view synthesis, with PSNR gains of up to 3.68 dB and FID reduced by nearly half. Our results highlight confidence-aware fusion of generative priors and support-view consistency as a key principle for robust feedforward 3D driving scene reconstruction.
