Stereophotoclinometry Revisited

• URL: http://arxiv.org/abs/2504.08252v1
• Date: Fri, 11 Apr 2025 04:33:56 GMT
• Title: Stereophotoclinometry Revisited
• Authors: Travis Driver, Andrew Vaughan, Yang Cheng, Adnan Ansar, John Christian, Panagiotis Tsiotras,
• Abstract summary: Photoclinometry-from-Motion (PhoMo) is a novel framework that incorporates photoclinometry techniques into a keypoint-based structure-from-motion system.<n>PhoMo estimates the surface normal and albedo at detected landmarks to improve autonomous surface and shape characterization of small celestial bodies.<n>The proposed framework is validated on real imagery taken by the Dawn mission to the asteroid 4 Vesta and the minor planet 1 Ceres.
• Score: 14.971236515078857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-based surface reconstruction and characterization is crucial for missions to small celestial bodies, as it informs mission planning, navigation, and scientific analysis. However, current state-of-the-practice methods, such as stereophotoclinometry (SPC), rely heavily on human-in-the-loop verification and high-fidelity a priori information. This paper proposes Photoclinometry-from-Motion (PhoMo), a novel framework that incorporates photoclinometry techniques into a keypoint-based structure-from-motion (SfM) system to estimate the surface normal and albedo at detected landmarks to improve autonomous surface and shape characterization of small celestial bodies from in-situ imagery. In contrast to SPC, we forego the expensive maplet estimation step and instead use dense keypoint measurements and correspondences from an autonomous keypoint detection and matching method based on deep learning. Moreover, we develop a factor graph-based approach allowing for simultaneous optimization of the spacecraft's pose, landmark positions, Sun-relative direction, and surface normals and albedos via fusion of Sun vector measurements and image keypoint measurements. The proposed framework is validated on real imagery taken by the Dawn mission to the asteroid 4 Vesta and the minor planet 1 Ceres and compared against an SPC reconstruction, where we demonstrate superior rendering performance compared to an SPC solution and precise alignment to a stereophotogrammetry (SPG) solution without relying on any a priori camera pose and topography information or humans-in-the-loop.

Related papers

• A New Statistical Model of Star Speckles for Learning to Detect and Characterize Exoplanets in Direct Imaging Observations [37.845442465099396]
  This paper presents a novel statistical model that captures nuisance fluctuations using a multi-scale approach.<n>It integrates into an interpretable, end-to-end learnable framework for simultaneous exoplanet detection and flux estimation.<n>The proposed approach is computationally efficient, robust to varying data quality, and well suited for large-scale observational surveys.
  arXiv  Detail & Related papers  (2025-03-21T13:07:55Z)
• Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions [40.13294159814764]
  Deconvolution of astronomical images is a key aspect of recovering the intrinsic properties of celestial objects.<n>This paper explores the use of diffusion models (DMs) and the Diffusion Posterior Sampling (DPS) algorithm to solve this inverse problem task.
  arXiv  Detail & Related papers  (2024-11-28T14:00:00Z)
• Keypoint-based Stereophotoclinometry for Characterizing and Navigating Small Bodies: A Factor Graph Approach [15.863759076104104]
  This paper proposes the incorporation of techniques from stereophotoclinometry into a keypoint-based structure-from-motion system. The proposed framework is validated on real imagery of the Cornelia crater on Asteroid 4 Vesta.
  arXiv  Detail & Related papers  (2023-12-11T22:23:43Z)
• View Consistent Purification for Accurate Cross-View Localization [59.48131378244399]
  This paper proposes a fine-grained self-localization method for outdoor robotics. The proposed method addresses limitations in existing cross-view localization methods. It is the first sparse visual-only method that enhances perception in dynamic environments.
  arXiv  Detail & Related papers  (2023-08-16T02:51:52Z)
• Towards Scalable Multi-View Reconstruction of Geometry and Materials [27.660389147094715]
  We propose a novel method for joint recovery of camera pose, object geometry and spatially-varying Bidirectional Reflectance Distribution Function (svBRDF) of 3D scenes. The input are high-resolution RGBD images captured by a mobile, hand-held capture system with point lights for active illumination.
  arXiv  Detail & Related papers  (2023-06-06T15:07:39Z)
• Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching [102.39635336450262]
  We address the problem of ground-to-satellite image geo-localization by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image.
  arXiv  Detail & Related papers  (2022-03-26T20:10:38Z)
• Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors [25.87104194833264]
  We propose a novel method for geo-tracking in outdoor environments by registering a vehicle's sensor information with aerial imagery of an unseen target region. We train a model in a metric learning setting to extract visual features from ground and aerial images. Our method is the first to utilize on-board cameras in an end-to-end differentiable model for metric self-localization on unseen orthophotos.
  arXiv  Detail & Related papers  (2022-03-07T12:25:44Z)
• Uncertainty-Aware Deep Multi-View Photometric Stereo [100.97116470055273]
  Photometric stereo (PS) is excellent at recovering high-frequency surface details, whereas multi-view stereo (MVS) can help remove the low-frequency distortion due to PS and retain the global shape. This paper proposes an approach that can effectively utilize such complementary strengths of PS and MVS. We estimate per-pixel surface normals and depth using an uncertainty-aware deep-PS network and deep-MVS network, respectively.
  arXiv  Detail & Related papers  (2022-02-26T05:45:52Z)
• Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images [65.74185962364211]
  We present a method for incorporating neighborhood information into distance-based dimensionality reduction methods. Based on a classification of different methods for comparing image patches, we explore a number of different approaches.
  arXiv  Detail & Related papers  (2022-02-18T13:17:43Z)
• Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo [103.08512487830669]
  We present a modern solution to the multi-view photometric stereo problem (MVPS) We procure the surface orientation using a photometric stereo (PS) image formation model and blend it with a multi-view neural radiance field representation to recover the object's surface geometry. Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network.
  arXiv  Detail & Related papers  (2021-10-11T20:20:03Z)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)

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.