Neural 3D Reconstruction of Planetary Surfaces from Descent-Phase Wide-Angle Imagery

Melonie de Almeida, George Brydon, Divya M Persaud, John H. Williamson, Paul Henderson

Keywords: 3D Reconstruction, Neural Radiance Fields, Digital Elevation Models, Planetary Surface Modeling

Abstract: 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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Submission Number: 48