用2D锚点生成一致的3D街景，解决噪声与不完整数据问题。
From Sparse and Imperfect 2D Anchors to Consistent 3D Gaussian Street Scenes: Support-Aware Appearance
- 通过教师-学生残差蒸馏，融合稀疏2D锚点与渲染结果
- 在跨视角编辑中实现90%以上的内容保真度和显著一致性提升
- 适合需要高质量3D街景重建的自动驾驶与城市建模场景
图像先验可为3D Gaussian街景合成目标条件，但独立编辑视图无法形成连贯3D结构。直接拟合会传播视图特异性噪声，而现有流程未能联合处理稀疏不完美锚点与标准光栅化部署。为此，提出教师相对外观残差蒸馏用于外观烘焙。通过教师锚点与原始渲染间的残差，构建频率分解、置信度估计与原型级提升的结构空间。渲染空间匹配提供直接优化信号，原型分配由支持感知的高斯空间聚合正则化。通过置信度门控的粗到精优化，支持细节被保留，非支持噪声被抑制，最终所有残差烘焙为固定几何的球谐系数。教师与辅助训练模块在推理时移除。在Waymo街景资产、Tanks and Temples场景及多种目标条件下评估显示，相较基于编辑的基线，在目标对齐、内容保真、伪影抑制与跨视图一致性上取得更优综合平衡。消融实验验证了核心组件有效性。代码将发布于https://github.com/Cagares/Baking-for-3D-Gaussian。
原文摘要 · Abstract (English)
Image priors can synthesize target conditions for 3D Gaussian street scenes, but independently edited views do not define a coherent 3D target. Direct fitting can propagate view-specific noise, while existing pipelines do not jointly handle imperfect sparse anchors and standard-rasterizer deployment. To address this gap, teacher-relative appearance residual distillation is introduced for appearance baking. A structured space for frequency decomposition, confidence estimation, and primitive-level lifting is formed by residuals between teacher anchors and original renders. The direct optimization signal is supplied by renderer-space matching, while primitive assignment is regularized by support-aware Gaussian-space aggregation. Supported detail is admitted and unsupported noise is suppressed through confidence-gated coarse-to-fine optimization, after which all residuals are baked into fixed-geometry spherical-harmonic coefficients. The teacher and auxiliary training modules are discarded at inference. Evaluation across Waymo street assets, Tanks and Temples scenes, and multiple target conditions shows a favorable overall balance of target alignment, content preservation, artifact suppression, and cross-view consistency over editing-based baselines. Ablations confirm the effectiveness of the main components. Code will be released at https://github.com/Cagares/Baking-for-3D-Gaussian.
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