TensoIR：张量逆渲染
TensoIR: Tensorial Inverse Rendering
AI总结：
提出TensoIR，基于张量分解与神经场，扩展TensoRF实现从多视图图像联合重建辐射场和物理模型，支持未知光照下的新视角合成与重光照，效果优于基线。
摘要：
我们提出了TensoIR，一种基于张量分解和神经场的新型逆渲染方法。与以往使用纯MLP神经场、因而受限于低容量和高计算成本的工作不同，我们将TensoRF（一种用于辐射场建模的最先进方法）扩展为从未知光照条件下捕获的多视角图像中估计场景几何、表面反射率和环境光照。我们的方法联合实现了辐射场重建和基于物理的模型估计，从而获得照片级真实感的新视角合成和重光照结果。得益于基于TensoRF表示的效率和可扩展性，我们的方法能够准确建模二次着色效果（如阴影和间接光照），并普遍支持在单一或多个未知光照条件下捕获的输入图像。低秩张量表示使我们不仅能够实现快速且紧凑的重建，还能在任意数量的捕获光照条件下更好地利用共享信息。我们在各种具有挑战性的合成和真实世界场景中，定性和定量地证明了我们方法相对于基线方法的优越性。
英文摘要：
We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields. Unlike previous works that use purely MLP-based neural fields, thus suffering from low capacity and high computation costs, we extend TensoRF, a state-of-the-art approach for radiance field modeling, to estimate scene geometry, surface reflectance, and environment illumination from multi-view images captured under unknown lighting conditions. Our approach jointly achieves radiance field reconstruction and physically-based model estimation, leading to photo-realistic novel view synthesis and relighting results. Benefiting from the efficiency and extensibility of the TensoRF-based representation, our method can accurately model secondary shading effects (like shadows and indirect lighting) and generally support input images captured under single or multiple unknown lighting conditions. The low-rank tensor representation allows us to not only achieve fast and compact reconstruction but also better exploit shared information under an arbitrary number of capturing lighting conditions. We demonstrate the superiority of our method to baseline methods qualitatively and quantitatively on various challenging synthetic and real-world scenes.
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