We propose RelitLRM, a Large Reconstruction Model (LRM) for generating high-quality Gaussian splatting representations of 3D objects under novel illuminations from sparse (4-8) posed images captured under unknown static lighting. Unlike prior inverse rendering methods requiring dense captures and slow optimization, often causing artifacts like incorrect highlights or shadow baking, RelitLRM adopts a feed-forward transformer-based model with a novel combination of a geometry reconstructor and a relightable appearance generator based on diffusion. The model is trained end-to-end on synthetic multi-view renderings of objects under varying known illuminations. This architecture design enables to effectively decompose geometry and appearance, resolve the ambiguity between material and lighting, and capture the multi-modal distribution of shadows and specularity in the relit appearance. We show our sparse-view feed-forward RelitLRM offers competitive relighting results to state-of-the-art dense-view optimization-based baselines while being significantly faster. Our project page is available at: https://relit-lrm.github.io/.

本研究针对传统逆渲染方法在重建3D物体时需大量图像和缓慢优化的缺陷，提出了RelitLRM，这是一种基于前馈变换器的新型大规模重建模型，能在稀疏图像下生成高质量的光照效果。研究表明，RelitLRM在速度上显著优于现有的密集优化基线，同时保持竞争力的可重光照效果，具备潜在的广泛应用前景。

RelitLRM：用于大型重建模型的生成可重光照辐射