首个统一支持三维重建、新视角合成与逆物理渲染的高效框架。
COREA: Coupled Relightable 3D Gaussians and SDFs for Efficient Normal Alignment
- 耦合SDF与可重光照3D高斯，共享表面几何信息。
- 在逆物理渲染任务中显著提升法向估计精度，优于现有方法。
- 适合需要高保真表面重建与光照还原的研究者使用。
我们提出COREA，首个统一支持基于球谐函数的新视角合成（NVS）、表面重建和逆物理渲染（inverse PBR）的三任务框架。该框架将SDF与可重光照3D高斯（3DGS）耦合于同一基础表面上，利用几何约束的3DGS提供可靠深度信号以锚定SDF几何，同时连续的SDF法向场为高斯法向学习提供空间一致的监督。通过深度引导对齐与法向感知的密度控制，结合双密度调控机制，在训练中平衡光度与几何梯度，实现稳定且内存高效的优化。在标准基准测试中，COREA是唯一能同时支持三项任务的框架，整体表现具有竞争力，尤其在逆物理渲染任务中表现突出。
原文摘要 · Abstract (English)
We present COREA, the first unified three-tasks framework that couples an SDF and relightable 3D Gaussians (3DGS) to jointly support SH-based novel-view synthesis (NVS), surface reconstruction, and inverse physically-based rendering (inverse PBR). While recent relightable 3DGS methods have progressed, inverse PBR remains bottlenecked by normal estimation, as the discrete nature of 3DGS often yields oversmoothed and unstable normals. To address this limitation, COREA couples the complementary geometric properties of an SDF and relightable 3DGS on a shared underlying surface, where geometry-constrained relightable 3DGS provides reliable depth signals to anchor SDF geometry and the continuous SDF normal field provides spatially consistent supervision for Gaussian normal learning. We couple these signals through depth-guided alignment and normal supervision with normal-aware densification, and introduce Dual-Density Control to regulate densification by balancing photometric and geometric gradients for stable, memory-efficient training. Experiments on standard benchmarks show that COREA is the only framework that supports all three tasks, achieving competitive performance overall, with particularly superior results in inverse PBR.
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