用置信度感知的扩散先验修复前馈3D高斯点云，提升自动驾驶场景重建质量
ConFixGS: Learning to Fix Feedforward 3D Gaussian Splatting with Confidence-Aware Diffusion Priors in Driving Scenes
- 通过扩散模型生成局部伪目标，结合多视角重投影验证置信度
- 在Waymo等数据集上实现最高3.68dB的PSNR提升，FID降低近一半
- 适合需要高鲁棒性前馈3D重建的自动驾驶视觉系统
前馈式3D高斯点云渲染（3DGS）在基于轨迹的稀疏视角驾驶场景中表现不佳。现有高斯修复方法多针对优化型3DGS，而基于扩散的修复通常局限于观测视角附近的迭代精修，前馈3DGS修复仍缺乏探索。本文提出ConFixGS，一种即插即用的修复方法，利用置信度感知的扩散先验来修正前馈3DGS。从预训练的前馈模型出发，ConFixGS生成扩散增强的局部伪目标，并通过支持视图的重投影交叉验证进行有效性检验。由此得到的稠密置信度图指导细节修复，强化可靠信息，抑制幻觉或不一致内容。在Waymo、nuScenes和KITTI数据集上，ConFixGS显著提升了具有挑战性的新视角合成性能，最高实现3.68 dB的PSNR增益，FID指标接近减半。结果表明，生成先验与支持视图一致性之间的置信度感知融合是实现鲁棒前馈3D驾驶场景重建的关键原则。
原文摘要 · Abstract (English)
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.
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