Abstract
Recent advances in neural scene representations have led to unprecedented quality in 3D reconstruction and view synthesis. Despite achieving high-quality results for common benchmarks with curated data, outputs often degrade for data that contain per image variations such as strong exposure changes, present, e.g., in most scenes with indoor and outdoor areas or rooms with windows. In this paper, we introduce Neural Exposure Fields (NExF), a novel technique for robustly reconstructing 3D scenes with high quality and 3D-consistent appearance from challenging real-world captures. In the core, we propose to learn a neural field predicting an optimal exposure value per 3D point, enabling us to optimize exposure along with the neural scene representation. While capture devices such as cameras select optimal exposure per image/pixel, we generalize this concept and perform optimization in 3D instead. This enables accurate view synthesis in high dynamic range scenarios, bypassing the need of post-processing steps or multi-exposure captures. Our contributions include a novel neural representation for exposure prediction, a system for joint optimization of the scene representation and the exposure field via a novel neural conditioning mechanism, and demonstrated superior performance on challenging real-world data. We find that our approach trains faster than prior works and produces state-of-the-art results on several benchmarks improving by over 55% over best-performing baselines.
Abstract (translated)
最近在神经场景表示领域的进展带来了前所未有的3D重建和视图合成质量。尽管对于精心策划的常见基准数据集取得了高质量的结果，但在包含单张图像变化的数据（例如强曝光变化）上，输出的质量往往会下降，这种情况通常出现在室内和室外区域或有窗户的房间中。在本文中，我们介绍了神经曝光场（NExF），这是一种新的技术，旨在从具有挑战性的现实世界捕获数据中以高质量和3D一致的外观稳健地重建3D场景。 我们的核心方法是学习一个预测每个3D点最优曝光值的神经场，从而允许我们在优化神经场景表示的同时进行曝光调整。与相机等捕捉设备选择每张图像/像素的最佳曝光不同，我们将这一概念进行了泛化，并在三维空间中进行优化。这使得在高动态范围场景中准确地合成视图成为可能，无需额外的后期处理步骤或多重曝光捕获。 我们的贡献包括一种用于预测曝光的新颖神经表示方法、一个通过新颖的神经调节机制共同优化场景表示和曝光场的系统，并且我们在具有挑战性的现实世界数据上展示了优于其他基线模型的优越性能。我们发现，与其他先前工作相比，我们的方法训练速度更快，并在多个基准测试中实现了比最佳基线高出55%以上的最新成果。
URL
https://arxiv.org/abs/2510.08279
