用神经网络重建行星着陆影像的三维地形，提升覆盖范围与精度
Neural 3D Reconstruction of Planetary Surfaces from Descent-Phase Wide-Angle Imagery
- 采用显式神经高程场建模，利用行星表面连续平滑的先验知识
- 在月球与火星模拟着陆序列上实现更高空间覆盖率和良好精度
- 适合行星地质研究与低成本高分辨率地形建模场景
行星表面的数字高程建模对研究过去和当前地质过程至关重要。着陆阶段获取的广角影像为高分辨率地形重建提供了低成本方案。然而，由于强烈的径向畸变以及垂直下降、主要朝下的相机带来的有限视差，从这类影像中进行精确三维重建极具挑战性。传统多视图立体视觉（MVS）在这些条件下深度范围受限、保真度下降，且缺乏领域先验。本文首次研究现代神经重建方法在行星着陆成像中的应用，提出一种结合显式神经高程场表示的新方法，该方法利用行星表面通常连续、平滑、固体且无漂浮物的先验特性。实验在高保真月球和火星地形的模拟着陆序列上进行，结果表明，所提方法在保持满意估计精度的同时，显著提升了空间覆盖范围，展现出对传统MVS方法的有力替代优势。
原文摘要 · Abstract (English)
Digital elevation modeling of planetary surfaces is essential for studying past and ongoing geological processes. Wide-angle imagery acquired during spacecraft descent promises to offer a low-cost option for high-resolution terrain reconstruction. However, accurate 3D reconstruction from such imagery is challenging due to strong radial distortion and limited parallax from vertically descending, predominantly nadir-facing cameras. Conventional multi-view stereo exhibits limited depth range and reduced fidelity under these conditions and also lacks domain-specific priors. We present the first study of modern neural reconstruction methods for planetary descent imaging. We also develop a novel approach that incorporates an explicit neural height field representation, which provides a strong prior since planetary surfaces are generally continuous, smooth, solid, and free from floating objects. This study demonstrates that neural approaches offer a strong and competitive alternative to traditional multi-view stereo (MVS) methods. Experiments on simulated descent sequences over high-fidelity lunar and Mars terrains demonstrate that the proposed approach achieves increased spatial coverage while maintaining satisfactory estimation accuracy.
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