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

• URL: http://arxiv.org/abs/2604.13235v1
• Date: Tue, 14 Apr 2026 19:06:22 GMT
• Title: Neural 3D Reconstruction of Planetary Surfaces from Descent-Phase Wide-Angle Imagery
• Abstract summary: We present the first study of modern neural reconstruction methods for planetary descent imaging.<n>We develop a novel approach that incorporates an explicit neural height field representation, which provides a strong prior.<n>Experiments on simulated descent sequences over high-fidelity lunar and Mars terrains demonstrate that the proposed approach achieves increased spatial coverage.
• Score: 5.951239817915668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Surfsvr: 2D Surface Priors as 3D Geometric Regularizers for Sparse Voxel Reconstruction [56.51413081953813]
  SurfSVR is a novel sparse voxel reconstruction paradigm that treats 2D surface priors as explicit 3D geometric regularizers.<n>It consistently improves sparse voxel reconstruction across scenes with substantially different visibility and geometry characteristics.
  arXiv  Detail & Related papers  (2026-08-12T11:25:31Z)
• Urban Neural Surface Reconstruction from Constrained Sparse Aerial Imagery with 3D SAR Fusion [5.462159447632879]
  We present the first framework that fuses 3D synthetic aperture radar point clouds with aerial imagery for high-fidelity reconstruction under constrained, sparse-view conditions.<n>Our framework integrates radar-derived spatial constraints into an SDF-based NSR backbone, guiding structure-aware ray selection and adaptive sampling for stable and efficient optimization.
  arXiv  Detail & Related papers  (2026-01-29T17:47:07Z)
• 3D Gaussian Flats: Hybrid 2D/3D Photometric Scene Reconstruction [62.84879632157956]
  We propose a novel hybrid 2D/3D representation that jointly optimize constrained planar (2D) Gaussians for modeling flat surfaces and freeform (3D) Gaussians for the rest of the scene.<n>Our end-to-end approach dynamically detects and refines planar regions, improving both visual fidelity and geometric accuracy.<n>It achieves state-of-the-art depth estimation on ScanNet++ and ScanNetv2, and excels at mesh extraction without overfitting to a specific camera model.
  arXiv  Detail & Related papers  (2025-09-19T21:04:36Z)
• Joint attitude estimation and 3D neural reconstruction of non-cooperative space objects [2.4078365225187386]
  3D models represent a valuable source of information in the field of Space Situational Awareness.<n>We leveraged Neural Radiance Fields (NeRF) to perform 3D reconstruction of non-cooperative space objects from simulated images.
  arXiv  Detail & Related papers  (2025-06-25T17:33:49Z)
• Geometric Prior-Guided Neural Implicit Surface Reconstruction in the Wild [13.109693095684921]
  We introduce a novel approach that applies multiple geometric constraints to the implicit surface optimization process.<n>First, we utilize sparse 3D points from structure-from-motion (SfM) to refine the signed distance function estimation for the reconstructed surface.<n>We also employ robust normal priors derived from a normal predictor, enhanced by edge prior filtering and multi-view consistency constraints.
  arXiv  Detail & Related papers  (2025-05-12T09:17:30Z)
• GCRayDiffusion: Pose-Free Surface Reconstruction via Geometric Consistent Ray Diffusion [30.773599974914415]
  Previous approaches have achieved impressive pose-free surface reconstruction results in dense-view settings.<n>We propose a new technique for pose-free surface reconstruction, which regularizes the learning by explicit points sampled from ray-based diffusion of camera pose estimation.<n>Our GCRayDiffusion achieves more accurate camera pose estimation than previous approaches, with geometrically more consistent surface reconstruction results.
  arXiv  Detail & Related papers  (2025-03-28T11:45:09Z)
• MonoGSDF: Exploring Monocular Geometric Cues for Gaussian Splatting-Guided Implicit Surface Reconstruction [86.87464903285208]
  We introduce MonoGSDF, a novel method that couples primitives with a neural Signed Distance Field (SDF) for high-quality reconstruction.<n>To handle arbitrary-scale scenes, we propose a scaling strategy for robust generalization.<n>Experiments on real-world datasets outperforms prior methods while maintaining efficiency.
  arXiv  Detail & Related papers  (2024-11-25T20:07:07Z)
• Reconstructing Satellites in 3D from Amateur Telescope Images [44.20773507571372]
  We propose a novel computational imaging framework that overcomes obstacles by integrating a hybrid image pre-processing pipeline.<n>We validate our approach on both synthetic satellite datasets and on-sky observations of China's Tiangong Space Station and the International Space Station.<n>Our framework enables high-fidelity 3D satellite monitoring from Earth, offering a cost-effective alternative for space situational awareness.
  arXiv  Detail & Related papers  (2024-04-29T03:13:09Z)
• Passive superresolution imaging of incoherent objects [63.942632088208505]
  Method consists of measuring the field's spatial mode components in the image plane in the overcomplete basis of Hermite-Gaussian modes and their superpositions. Deep neural network is used to reconstruct the object from these measurements.
  arXiv  Detail & Related papers  (2023-04-19T15:53:09Z)
• MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction [72.05649682685197]
  State-of-the-art neural implicit methods allow for high-quality reconstructions of simple scenes from many input views. This is caused primarily by the inherent ambiguity in the RGB reconstruction loss that does not provide enough constraints. Motivated by recent advances in the area of monocular geometry prediction, we explore the utility these cues provide for improving neural implicit surface reconstruction.
  arXiv  Detail & Related papers  (2022-06-01T17:58:15Z)
• Neural 3D Reconstruction in the Wild [86.6264706256377]
  We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections. We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
  arXiv  Detail & Related papers  (2022-05-25T17:59:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.