COREA: Coarse-to-Fine 3D Representation Alignment Between Relightable 3D Gaussians and SDF via Bidirectional 3D-to-3D Supervision
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.
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Jaeyoon Lee
claimed_verified
2025-12-10T08:57:00.740Z
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Jaeyoon Lee
pax2034
user
pax2034
6937dfe319d912300c34a4d7
Hojoon Jung
claimed_verified
2025-12-10T08:56:58.779Z
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HojoonJung
HojoonJung
user
HojoonJung
6937dfe319d912300c34a4d8
Sungtae Hwang
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Jihyong Oh
claimed_verified
2025-12-09T10:34:37.557Z
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Jihyong Oh
ozbro
user
ozbro
6937dfe319d912300c34a4da
Jongwon Choi
COREA: Coarse-to-Fine 3D Representation Alignment Between Relightable 3D Gaussians and SDF via Bidirectional 3D-to-3D Supervision
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 is a unified framework that jointly learns 3D Gaussians and a Signed Distance Field for accurate geometry reconstruction and faithful relighting, using a coarse-to-fine bidirectional 3D-to-3D alignment strategy.
relightable 3D Gaussians Signed Distance Field (SDF) geometry reconstruction faithful relighting 3D Gaussian Splatting (3DGS) mesh reconstruction physically-based rendering (PBR) coarse-to-fine bidirectional 3D-to-3D alignment depth gradients normals density-control mechanism novel-view synthesis