From Images2Mesh: A 3D Surface Reconstruction Pipeline for Non-Cooperative Space Objects

• URL: http://arxiv.org/abs/2605.00147v1
• Date: Thu, 30 Apr 2026 19:10:29 GMT
• Title: From Images2Mesh: A 3D Surface Reconstruction Pipeline for Non-Cooperative Space Objects
• Abstract summary: We present a pipeline for neural implicit surface reconstruction of non-cooperative space objects from monocular inspection imagery.<n>We demonstrate it on publicly released ISS inspection footage from the STS-119 mission and publicly released on-orbit inspection footage of an H-IIA rocket upper stage.<n>We find that segmentation-based background removal is essential for successful camera pose estimation from real on-orbit footage.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: On-orbit inspection imagery is crucial as it enables characterization of non-cooperative resident space objects, providing the geometry and structural condition essential for active debris removal and on-orbit servicing mission planning. However, most existing neural implicit surface reconstruction methods have been confined to synthetic or hardware-in-the-loop data with known camera poses and controlled illumination. In this work, we present a pipeline for neural implicit surface reconstruction of non-cooperative space objects from monocular inspection imagery. We demonstrate it on publicly released ISS inspection footage from the STS-119 mission and publicly released on-orbit inspection footage of an H-IIA rocket upper stage. We find that segmentation-based background removal is essential for successful camera pose estimation from real on-orbit footage, where background variation between frames caused direct processing to fail entirely. We further incorporate photometric correction of per-frame exposure variations and analyze its behavior across datasets, finding that performance in shadowed regions varies with the illumination characteristics of the input footage.

Related papers

• SeeSE3: Emergence of 3D Space in Vision Features [54.140615953727355]
  We investigate the relation between the structure of the space of visual features and the group of Euclidean transformations $SE(3)$.<n>We show that self-supervised vision models, which, in principle, have not been trained with direct 3D supervision or active agency, possess latent subspaces that are remarkably strongly correlated with 3D Euclidean space, when probed correctly.<n>We propose a new class of "Latent-Space Navigation" techniques that perform visual odometry and localization purely in the latent space, bypassing the need for explicit 3D reconstruction.
  arXiv  Detail & Related papers  (2026-07-15T18:00:31Z)
• SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing [85.49998134609665]
  Image spatial editing performs geometry-driven transformations, allowing precise control over object layout and camera viewpoints.<n>Current models are insufficient for fine-grained spatial manipulations, motivating a dedicated assessment suite.<n>We introduce SpatialEdit-Bench, a complete benchmark that evaluates spatial editing.<n>We construct SpatialEdit-500k, a synthetic dataset generated with a controllable Blender pipeline.
  arXiv  Detail & Related papers  (2026-04-06T17:54:42Z)
• A 3D Reconstruction Benchmark for Asset Inspection [39.16724739487446]
  We present a new dataset with ground truth depth maps, camera poses, and mesh models of three synthetic scenes.<n>We evaluate state-of-the-art reconstruction methods on this dataset.
  arXiv  Detail & Related papers  (2026-03-18T04:42:14Z)
• Improved 3D Gaussian Splatting of Unknown Spacecraft Structure Using Space Environment Illumination Knowledge [7.2620484413601325]
  This work presents a novel pipeline to recover the 3D structure of an unknown target spacecraft from a sequence of images captured in space.<n>Learning 3DGS requires static scenes, an assumption in contrast to dynamic lighting conditions encountered in spaceborne imagery.<n>The trained 3DGS model can also be used for camera pose estimation through photometric optimization.
  arXiv  Detail & Related papers  (2025-12-30T05:35:35Z)
• 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)
• 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)
• Decompositional Neural Scene Reconstruction with Generative Diffusion Prior [64.71091831762214]
  Decompositional reconstruction of 3D scenes, with complete shapes and detailed texture, is intriguing for downstream applications.<n>Recent approaches incorporate semantic or geometric regularization to address this issue, but they suffer significant degradation in underconstrained areas.<n>We propose DP-Recon, which employs diffusion priors in the form of Score Distillation Sampling (SDS) to optimize the neural representation of each individual object under novel views.
  arXiv  Detail & Related papers  (2025-03-19T02:11:31Z)
• SMORE: Simultaneous Map and Object REconstruction [66.66729715211642]
  We present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR.<n>We take a holistic perspective and optimize a compositional model of a dynamic scene that decomposes the world into rigidly-moving objects and the background.
  arXiv  Detail & Related papers  (2024-06-19T23:53:31Z)
• 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)
• On the Generation of a Synthetic Event-Based Vision Dataset for Navigation and Landing [69.34740063574921]
  This paper presents a methodology for generating event-based vision datasets from optimal landing trajectories. We construct sequences of photorealistic images of the lunar surface with the Planet and Asteroid Natural Scene Generation Utility. We demonstrate that the pipeline can generate realistic event-based representations of surface features by constructing a dataset of 500 trajectories.
  arXiv  Detail & Related papers  (2023-08-01T09:14:20Z)
• The Drunkard's Odometry: Estimating Camera Motion in Deforming Scenes [79.00228778543553]
  This dataset is the first large set of exploratory camera trajectories with ground truth inside 3D scenes. Simulations in realistic 3D buildings lets us obtain a vast amount of data and ground truth labels. We present a novel deformable odometry method, dubbed the Drunkard's Odometry, which decomposes optical flow estimates into rigid-body camera motion.
  arXiv  Detail & Related papers  (2023-06-29T13:09:31Z)
• Towards Non-Line-of-Sight Photography [48.491977359971855]
  Non-line-of-sight (NLOS) imaging is based on capturing the multi-bounce indirect reflections from the hidden objects. Active NLOS imaging systems rely on the capture of the time of flight of light through the scene. We propose a new problem formulation, called NLOS photography, to specifically address this deficiency.
  arXiv  Detail & Related papers  (2021-09-16T08:07:13Z)

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.