Refined Equivalent Pinhole Model for Large-scale 3D Reconstruction from Spaceborne CCD Imagery

• URL: http://arxiv.org/abs/2310.20117v1
• Date: Tue, 31 Oct 2023 01:30:57 GMT
• Title: Refined Equivalent Pinhole Model for Large-scale 3D Reconstruction from Spaceborne CCD Imagery
• Authors: Hong Danyang, Yu Anzhu, Ji Song, Cao Xuefeng, Quan Yujun, Guo Wenyue, Qiu Chunping
• Abstract summary: We present a large-scale earth surface reconstruction pipeline for linear-array charge-coupled satellite imagery. Results demonstrated that the reconstruction accuracy was proportional to the image size. Our image refinement model significantly enhanced the accuracy and completeness of the reconstruction.
• Score: 1.4019041243188557
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, we present a large-scale earth surface reconstruction pipeline for linear-array charge-coupled device (CCD) satellite imagery. While mainstream satellite image-based reconstruction approaches perform exceptionally well, the rational functional model (RFM) is subject to several limitations. For example, the RFM has no rigorous physical interpretation and differs significantly from the pinhole imaging model; hence, it cannot be directly applied to learning-based 3D reconstruction networks and to more novel reconstruction pipelines in computer vision. Hence, in this study, we introduce a method in which the RFM is equivalent to the pinhole camera model (PCM), meaning that the internal and external parameters of the pinhole camera are used instead of the rational polynomial coefficient parameters. We then derive an error formula for this equivalent pinhole model for the first time, demonstrating the influence of the image size on the accuracy of the reconstruction. In addition, we propose a polynomial image refinement model that minimizes equivalent errors via the least squares method. The experiments were conducted using four image datasets: WHU-TLC, DFC2019, ISPRS-ZY3, and GF7. The results demonstrated that the reconstruction accuracy was proportional to the image size. Our polynomial image refinement model significantly enhanced the accuracy and completeness of the reconstruction, and achieved more significant improvements for larger-scale images.

Related papers

• GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement [51.97726804507328]
  We propose a novel approach for 3D mesh reconstruction from multi-view images. Our method takes inspiration from large reconstruction models that use a transformer-based triplane generator and a Neural Radiance Field (NeRF) model trained on multi-view images.
  arXiv  Detail & Related papers  (2024-06-09T05:19:24Z)
• Imaging Signal Recovery Using Neural Network Priors Under Uncertain Forward Model Parameters [0.7724713939814069]
  Inverse imaging problems (IIPs) arise in various applications, with the main objective of reconstructing an image from its compressed measurements. We propose a novel Moment-Aggregation (MA) framework that is compatible with the popular IIP solution by using a neural network prior. We theoretically demonstrate the convergence of the MA framework, which has a similar complexity with reconstruction under the known forward model parameters.
  arXiv  Detail & Related papers  (2024-05-05T14:12:48Z)
• A Generative Machine Learning Model for Material Microstructure 3D Reconstruction and Performance Evaluation [4.169915659794567]
  The dimensional extension from 2D to 3D is viewed as a highly challenging inverse problem from the current technological perspective. A novel generative model that integrates the multiscale properties of U-net with and the generative capabilities of GAN has been proposed. The model's accuracy is further improved by combining the image regularization loss with the Wasserstein distance loss.
  arXiv  Detail & Related papers  (2024-02-24T13:42:34Z)
• Latent Diffusion Prior Enhanced Deep Unfolding for Snapshot Spectral Compressive Imaging [17.511583657111792]
  Snapshot spectral imaging reconstruction aims to reconstruct three-dimensional spatial-spectral images from a single-shot two-dimensional compressed measurement. We introduce a generative model, namely the latent diffusion model (LDM), to generate degradation-free prior to deep unfolding method.
  arXiv  Detail & Related papers  (2023-11-24T04:55:20Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• A Model for Multi-View Residual Covariances based on Perspective Deformation [88.21738020902411]
  We derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups. We validate our model with synthetic and real data and integrate it into photometric and feature-based Bundle Adjustment.
  arXiv  Detail & Related papers  (2022-02-01T21:21:56Z)
• Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling [0.0]
  We develop a 3D Enhanced Deep Super Resolution (EDSR) convolutional neural network to create enhanced, high-resolution data over large spatial scales. We validate the network with various metrics: textual analysis, segmentation behaviour and pore-network model (PNM) multiphase flow simulations. The EDSR generated model is more accurate than the base LR model at predicting experimental behaviour in the presence of heterogeneities.
  arXiv  Detail & Related papers  (2021-11-01T21:49:22Z)
• 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop [128.07841893637337]
  Regression-based methods have recently shown promising results in reconstructing human meshes from monocular images. Minor deviation in parameters may lead to noticeable misalignment between the estimated meshes and image evidences. We propose a Pyramidal Mesh Alignment Feedback (PyMAF) loop to leverage a feature pyramid and rectify the predicted parameters.
  arXiv  Detail & Related papers  (2021-03-30T17:07:49Z)
• PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction [67.08350202974434]
  We propose Parametric Model-Conditioned Implicit Representation (PaMIR), which combines the parametric body model with the free-form deep implicit function. We show that our method achieves state-of-the-art performance for image-based 3D human reconstruction in the cases of challenging poses and clothing types.
  arXiv  Detail & Related papers  (2020-07-08T02:26:19Z)
• Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline [100.5353614588565]
  We propose to incorporate the domain knowledge of the LDR image formation pipeline into our model. We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization. We demonstrate that the proposed method performs favorably against state-of-the-art single-image HDR reconstruction algorithms.
  arXiv  Detail & Related papers  (2020-04-02T17:59:04Z)

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.