COREA：基于双向3D到3D监督的可重光照3D高斯与SDF之间的粗到精三维表征对齐

摘要

我们提出COREA，这是首个能够联合学习可重光照三维高斯模型与符号距离场（SDF）的统一框架，旨在实现精确的几何重建与真实的重光照效果。尽管近期三维高斯泼溅（3DGS）方法已拓展至网格重建与基于物理的渲染（PBR），但其几何信息仍从二维渲染中学习，导致表面粗糙且BRDF-光照分解不可靠。为解决这些局限，COREA引入了由粗到精的双向三维对齐策略，使几何信号能在三维空间中直接学习。该策略通过深度实现两种表征的粗对齐，利用深度梯度与法向量细化微观结构，最终生成的几何体为稳定的BRDF-光照分解提供支撑。密度控制机制进一步稳定高斯分布的生长，在几何保真度与内存效率间取得平衡。在标准基准测试上的实验表明，COREA在统一框架内实现了新颖视角合成、网格重建与PBR的卓越性能。

English

We present COREA, the first unified framework that jointly learns relightable 3D Gaussians and a Signed Distance Field (SDF) for accurate geometry reconstruction and faithful relighting. While recent 3D Gaussian Splatting (3DGS) methods have extended toward mesh reconstruction and physically-based rendering (PBR), their geometry is still learned from 2D renderings, leading to coarse surfaces and unreliable BRDF-lighting decomposition. To address these limitations, COREA introduces a coarse-to-fine bidirectional 3D-to-3D alignment strategy that allows geometric signals to be learned directly in 3D space. Within this strategy, depth provides coarse alignment between the two representations, while depth gradients and normals refine fine-scale structure, and the resulting geometry supports stable BRDF-lighting decomposition. A density-control mechanism further stabilizes Gaussian growth, balancing geometric fidelity with memory efficiency. Experiments on standard benchmarks demonstrate that COREA achieves superior performance in novel-view synthesis, mesh reconstruction, and PBR within a unified framework.

COREA：基于双向3D到3D监督的可重光照3D高斯与SDF之间的粗到精三维表征对齐

COREA: Coarse-to-Fine 3D Representation Alignment Between Relightable 3D Gaussians and SDF via Bidirectional 3D-to-3D Supervision

摘要

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