Neural 3D Reconstruction of Planetary Surfaces from Descent-Phase Wide-Angle Imagery
For planetary science missions, this work provides a low-cost, high-resolution terrain reconstruction method from descent imagery, though results are limited to simulated data.
This paper tackles 3D reconstruction of planetary surfaces from descent-phase wide-angle imagery, a challenging task due to radial distortion and limited parallax. The proposed neural height field approach achieves increased spatial coverage with satisfactory accuracy, outperforming traditional multi-view stereo methods.
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.