The task of generating novel views of real scenes is increasingly important nowadays when AI models become able to create realistic new worlds. In many practical applications, it is important for novel view synthesis methods to stay grounded in the physical world as much as possible, while also being able to imagine it from previously unseen views. While most current methods are developed and tested in virtual environments with small scenes and no errors in pose and depth information, we push the boundaries to the real-world domain of large scales in the new context of UAVs. Our algorithmic contributions are two folds. First, we manage to stay anchored in the real 3D world, by introducing an efficient multi-scale voxel carving method, which is able to accommodate significant noises in pose, depth, and illumination variations, while being able to reconstruct the view of the world from drastically different poses at test time. Second, our final high-resolution output is efficiently self-trained on data automatically generated by the voxel carving module, which gives it the flexibility to adapt efficiently to any scene. We demonstrated the effectiveness of our method on highly complex and large-scale scenes in real environments while outperforming the current state-of-the-art. Our code is publicly available: https://github.com/onorabil/MSVC.

翻译：近年来，随着AI模型能够创建逼真的新世界，从真实场景生成新的视角变得越来越重要。在许多实际应用中，新型视图合成方法需要在尽可能贴近物理世界的同时，也能从未见过的视角进行想象。虽然当前大多数方法是在虚拟环境中开发并测试的，且场景规模小、姿态和深度信息无误差，但我们突破极限，将研究扩展到无人机背景下的大尺度真实世界领域。我们的算法贡献有两个方面。首先，我们通过引入高效的多尺度体素雕刻方法，成功锚定在真实三维世界中。该方法能够容忍姿态、深度和光照变化中的显著噪声，同时在测试时能够从截然不同的姿态重建世界视图。其次，我们的高分辨率最终输出通过对体素雕刻模块自动生成的数据进行高效的自训练，从而能够灵活适应任何场景。我们在真实环境中高度复杂的大尺度场景上证明了方法的有效性，并超越了当前最先进的技术。我们的代码已公开：https://github.com/onorabil/MSVC。