LANe：用于组合场景合成的光照感知神经场
LANe: Lighting-Aware Neural Fields for Compositional Scene Synthesis
AI总结：
提出光照感知神经场LANe，通过解耦世界与物体神经场并结合光场与着色器处理光照变化，实现了在空间光照变化下物理一致的驾驶场景组合合成。
摘要：
神经场近期在表示和渲染3D场景方面取得了巨大成功。然而，大多数最先进的隐式表示将静态或动态场景作为一个整体进行建模，仅有微小的变化。现有关于学习解耦世界和物体神经场的工作，并未考虑以光照感知的方式将物体组合到不同的世界神经场中的问题。我们提出了光照感知神经场（LANe），以物理一致的方式实现驾驶场景的组合合成。具体而言，我们学习一种场景表示，将静态背景和瞬态元素解耦为world-NeRF和特定类别的object-NeRFs，从而允许对场景中的多个物体进行组合合成。此外，我们显式设计了世界和物体模型以处理光照变化，这使我们能够将物体组合到具有空间变化光照的场景中。这是通过构建场景的光场并将其与学习到的着色器结合使用以调制object NeRFs的外观来实现的。我们在使用CARLA模拟器渲染的具有多种光照条件的合成数据集，以及在不同时间收集的汽车真实世界新数据集上，展示了我们模型的性能。我们的方法表明，在具有挑战性的数据集设置上，它优于最先进的组合场景合成方法，其方式是将从一个场景学习到的object-NeRFs组合到一个完全不同的场景中，同时仍然遵循新场景中的光照变化。如需更多结果，请访问我们的项目网站https://lane-composition.github.io/。
英文摘要：
Neural fields have recently enjoyed great success in representing and rendering 3D scenes. However, most state-of-the-art implicit representations model static or dynamic scenes as a whole, with minor variations. Existing work on learning disentangled world and object neural fields do not consider the problem of composing objects into different world neural fields in a lighting-aware manner. We present Lighting-Aware Neural Field (LANe) for the compositional synthesis of driving scenes in a physically consistent manner. Specifically, we learn a scene representation that disentangles the static background and transient elements into a world-NeRF and class-specific object-NeRFs to allow compositional synthesis of multiple objects in the scene. Furthermore, we explicitly designed both the world and object models to handle lighting variation, which allows us to compose objects into scenes with spatially varying lighting. This is achieved by constructing a light field of the scene and using it in conjunction with a learned shader to modulate the appearance of the object NeRFs. We demonstrate the performance of our model on a synthetic dataset of diverse lighting conditions rendered with the CARLA simulator, as well as a novel real-world dataset of cars collected at different times of the day. Our approach shows that it outperforms state-of-the-art compositional scene synthesis on the challenging dataset setup, via composing object-NeRFs learned from one scene into an entirely different scene whilst still respecting the lighting variations in the novel scene. For more results, please visit our project website https://lane-composition.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
