用神经渲染直接从卫星图像生成高精度数字地形图。
Construction of Digital Terrain Maps from Multi-view Satellite Imagery using Neural Volume Rendering
- 无需深度或结构先验，仅凭图像位置学习纹理化地形
- 在真实地球与火星数据上实现接近卫星分辨率的地形精度
- 适合需要快速生成高质地形图的行星探测任务
数字地形图（DTMs）是行星探测的重要基础，支持航天器着陆时的地形相对导航及地面导航。随着机器人探测任务日益复杂，对高质量DTM的需求持续上升。然而，当前基于多视角立体匹配的DTM生成流程繁琐，需大量人工图像预处理才能获得满意结果。本文提出神经地形图（NTM）方法，通过神经体积渲染技术，直接从卫星影像中学习带纹理的数字地形图，仅需每像素的图像位置信息，不依赖深度或其他结构先验。我们在涵盖约100 km²的地球与火星真实及合成卫星数据上验证该方法。通过与传统多视图立体管道生成的高质量DTM对比，结果表明：即使在相机内参外参不完美情况下，本方法预测地形的精度几乎达到卫星图像分辨率，展现出良好潜力。
原文摘要 · Abstract (English)
Digital terrain maps (DTMs) are an important part of planetary exploration, enabling operations such as terrain relative navigation during entry, descent, and landing for spacecraft and aiding in navigation on the ground. As robotic exploration missions become more ambitious, the need for high quality DTMs will only increase. However, producing DTMs via multi-view stereo pipelines for satellite imagery, the current state-of-the-art, can be cumbersome and require significant manual image preprocessing to produce satisfactory results. In this work, we seek to address these shortcomings by adapting neural volume rendering techniques to learn textured digital terrain maps directly from satellite imagery. Our method, neural terrain maps (NTM), only requires the locus for each image pixel and does not rely on depth or any other structural priors. We demonstrate our method on both synthetic and real satellite data from Earth and Mars encompassing scenes on the order of $100 \textrm{km}^2$. We evaluate the accuracy of our output terrain maps by comparing with existing high-quality DTMs produced using traditional multi-view stereo pipelines. Our method shows promising results, with the precision of terrain prediction almost equal to the resolution of the satellite images even in the presence of imperfect camera intrinsics and extrinsics.
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