mlsanityData to 14 Oct 2026

Camera Movement Estimation and Path Correction using the Combination of Modified A-SIFT and Stereo System for 3D Modelling

arXiv 2503.17668 ↗First posted 22 Mar 2025cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Creating accurate and efficient 3D models poses significant challenges, particularly in addressing large viewpoint variations, computational complexity, and alignment discrepancies. Efficient camera path generation can help resolve these issues. In this context, a modified version of the Affine Scale-Invariant Feature Transform (ASIFT) is proposed to extract more matching points with reduced computational overhead, ensuring an adequate number of inliers for precise camera rotation angle estimation. Additionally, a novel two-camera-based rotation correction model is introduced to mitigate small rotational errors, further enhancing accuracy. Furthermore, a stereo camera-based translation estimation and correction model is implemented to determine camera movement in 3D space by altering the Structure From Motion (SFM) model. Finally, the novel combination of ASIFT and two camera-based SFM models provides an accurate camera movement trajectory in 3D space. Experimental results show that the proposed camera movement approach achieves 99.9% accuracy compared to the actual camera movement path and outperforms state-of-the-art camera path estimation methods. By leveraging this accurate camera path, the system facilitates the creation of precise 3D models, making it a robust solution for applications requiring high fidelity and efficiency in 3D reconstruction.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 46 references that are in the map. First posted on arXiv 22 Mar 2025.

Builds on

13 of 46 references.

References · 13

  1. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 988
  2. 2023Point-SLAM: Dense Neural Point Cloud-based SLAMcited by 160
  3. 2018Layer-structured 3D Scene Inference via View Synthesiscited by 82
  4. 2022State of the Art in Dense Monocular Non-Rigid 3D Reconstructioncited by 30
  5. 2021A Decomposition Model for Stereo Matchingcited by 25
  6. 2021PLUMENet: Efficient 3D Object Detection from Stereo Imagescited by 19
  7. 2016Multi-body Non-rigid Structure-from-Motioncited by 14
  8. 2023Rethinking the Approximation Error in 3D Surface Fitting for Point Cloud Normal Estimationcited by 13
  9. 2022Level-S2fM: Structure from Motion on Neural Level Set of Implicit Surfacescited by 9
  10. 2019NRMVS: Non-Rigid Multi-View Stereocited by 5
  11. 2020Dense Non-Rigid Structure from Motion: A Manifold Viewpointcited by 4
  12. 2019Toward 3D Object Reconstruction from Stereo Imagescited by 2
1 more
  1. 2022End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumescited by 2

Built on by

Not cited yet.