Mapping the terrain and understory hidden beneath dense forest canopies is of great interest for numerous applications such as search and rescue, trail mapping, forest inventory tasks, and more. Existing solutions rely on specialized sensors: either heavy, costly airborne LiDAR, or Airborne Optical Sectioning (AOS), which uses thermal synthetic aperture photography and is tailored for person detection. We introduce a novel approach for the reconstruction of canopy-free, photorealistic ground views using only conventional RGB images. Our solution is based on the celebrated Neural Radiance Fields (NeRF), a recent 3D reconstruction method. Additionally, we include specific image capture considerations, which dictate the needed illumination to successfully expose the scene beneath the canopy. To better cope with the poorly lit understory, we employ a low light loss. Finally, we propose two complementary approaches to remove occluding canopy elements by controlling per-ray integration procedure. To validate the value of our approach, we present two possible downstream tasks. For the task of search and rescue (SAR), we demonstrate that our method enables person detection which achieves promising results compared to thermal AOS (using only RGB images). Additionally, we show the potential of our approach for forest inventory tasks like tree counting. These results position our approach as a cost-effective, high-resolution alternative to specialized sensors for SAR, trail mapping, and forest-inventory tasks.

翻译：绘制茂密林冠层下隐藏的地形与林下植被，对于搜救、步道测绘、森林资源调查等诸多应用具有重要意义。现有解决方案依赖于专用传感器：要么是笨重昂贵的机载激光雷达，要么是采用热合成孔径摄影技术并专为人员检测设计的机载光学切片技术。我们提出了一种仅使用常规RGB图像重建无冠层、逼真地面视图的新方法。我们的解决方案基于近年来备受瞩目的三维重建方法——神经辐射场。此外，我们提出了特定的图像采集考量，明确了成功曝光林冠下场景所需的光照条件。为更好地应对光照不足的林下环境，我们采用了低光照损失函数。最后，我们提出了两种互补的方法，通过控制每条光线的积分过程来移除遮挡性的冠层要素。为验证本方法的价值，我们展示了两种潜在的下游任务。在搜救任务中，我们证明该方法能够实现人员检测，与热合成孔径摄影技术相比取得了有希望的结果。此外，我们展示了该方法在树木计数等森林资源调查任务中的应用潜力。这些结果表明，对于搜救、步道测绘和森林资源调查任务，我们的方法可作为一种高分辨率、高性价比的专用传感器替代方案。