COREA：基於雙向三維到三維監督的可重光照三維高斯分佈與符號距離函數的粗細粒度三維表徵對齊

摘要

我們提出COREA，這是首個能夠聯合學習可重光照三維高斯模型與符號距離場（SDF）的統一框架，實現精確的幾何重建與真實的重光照效果。儘管近期三維高斯潑濺（3DGS）方法已擴展至網格重建與基於物理的渲染（PBR），但其幾何資訊仍從二維渲染中學習，導致表面粗糙且BRDF-光照分解不可靠。為解決這些局限，COREA引入由粗到精的雙向三維對齊策略，使幾何信號能直接在三维空間中學習。該策略中，深度資訊提供兩種表徵間的粗對齊，而深度梯度與法向量則優化細尺度結構，最終生成的幾何體支持穩定的BRDF-光照分解。此外，密度控制機制進一步穩定高斯模型生長，平衡幾何保真度與記憶體效率。在標準基準測試上的實驗表明，COREA在統一框架內實現了優異的新視角合成、網格重建和PBR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：基於雙向三維到三維監督的可重光照三維高斯分佈與符號距離函數的粗細粒度三維表徵對齊

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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