RouteBridge：神经辐射场与3D高斯泼溅之间的可靠性路由双向蒸馏

中文摘要 AI 辅助

神经辐射场（NeRF）和3D高斯泼溅（3DGS）以互补的归纳偏置编码场景，但现有的跨表示蒸馏通常将一种表示固定为整个场景的教师。全局固定的教师可能传播局部重建误差。我们提出RouteBridge，一种双向框架，为每条光线选择教学方向。其可靠性估计器结合光度残差与表示特定的几何证据，并将监督从NeRF路由到3DGS、从3DGS路由到NeRF，或弃权（不执行）。一个与渲染器无关的接口在不共享特征或点对应的情况下传递颜色、不透明度和归一化深度。在mip-NeRF 360上，NeRF和3DGS导出分别达到28.56和28.77 dB。3DGS导出比3DGS提高1.56 dB，比NeRF-GS提高0.45 dB，同时将LPIPS降低至0.207。在静态三视图DTU上，RouteBridge获得21.12 dB。消融实验表明，自适应路由和几何光线目标均有助于改进。

英文摘要

Neural radiance fields (NeRFs) and 3D Gaussian Splatting (3DGS) encode a scene with complementary inductive biases, but existing cross-representation distillation typically fixes one representation as teacher for the entire scene. A globally fixed teacher can propagate local reconstruction errors. We present RouteBridge, a bidirectional framework that selects the teaching direction for each ray. Its reliability estimator combines photometric residuals with representation-specific geometric evidence and routes supervision from NeRF to 3DGS, from 3DGS to NeRF, or abstains. A renderer-independent interface transfers color, opacity, and normalized depth without shared features or point correspondence. On mip-NeRF 360, the NeRF and 3DGS exports reach 28.56 and 28.77 dB, respectively. The 3DGS export improves over 3DGS by 1.56 dB and over NeRF-GS by 0.45 dB while reducing LPIPS to 0.207. On static three-view DTU, RouteBridge obtains 21.12 dB. Ablations show that both adaptive routing and geometric ray targets contribute to the improvement.

发表机构

• University of Technology Sydney（悉尼科技大学）

机构由 AI 辅助整理，请以论文原文为准。