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

• URL: http://arxiv.org/abs/2503.17668v2
• Date: Fri, 23 May 2025 10:09:21 GMT
• Title: Camera Movement Estimation and Path Correction using the Combination of Modified A-SIFT and Stereo System for 3D Modelling
• Authors: Usha Kumari, Shuvendu Rana,
• Abstract summary: Efficient camera path generation can help resolve issues in creating accurate and efficient 3D models.<n>A modified version of the Affine Scale-Invariant Feature Transform (ASIFT) is proposed to extract more matching points with reduced computational overhead.<n>A novel two-camera-based rotation correction model is introduced to mitigate small rotational errors.<n>A stereo camera-based translation estimation and correction model is implemented to determine camera movement in 3D space.
• Score: 1.6574413179773757
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• PCR-GS: COLMAP-Free 3D Gaussian Splatting via Pose Co-Regularizations [102.0476991174456]
  COLMAP-free 3DGS has attracted increasing attention due to its remarkable performance in reconstructing high-quality 3D scenes from unposed images or videos.<n>We propose PCR-GS, an innovative COLMAP-free 3DGS technique that achieves superior 3D scene modeling and camera pose estimation via camera pose co-regularization.
  arXiv  Detail & Related papers  (2025-07-18T13:09:33Z)
• 3D Trajectory Reconstruction of Moving Points Based on Asynchronous Cameras [6.9017898687323775]
  Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties.<n>This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras.
  arXiv  Detail & Related papers  (2025-05-31T13:04:31Z)
• UniK3D: Universal Camera Monocular 3D Estimation [62.06785782635153]
  We present UniK3D, the first generalizable method for monocular 3D estimation able to model any camera.<n>Our method introduces a spherical 3D representation which allows for better disentanglement of camera and scene geometry.<n>A comprehensive zero-shot evaluation on 13 diverse datasets demonstrates the state-of-the-art performance of UniK3D across 3D, depth, and camera metrics.
  arXiv  Detail & Related papers  (2025-03-20T17:49:23Z)
• CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images [19.08403715388913]
  A critical issue is the camera motion blur caused by movement during exposure, which hinders accurate 3D scene reconstruction.<n>We propose CoMoGaussian, a Continuous Motion-Aware Gaussian Splatting that reconstructs precise 3D scenes from motion-red images.
  arXiv  Detail & Related papers  (2025-03-07T11:18:43Z)
• Automatic Calibration of a Multi-Camera System with Limited Overlapping Fields of View for 3D Surgical Scene Reconstruction [0.7165255458140439]
  The purpose of this study is to develop an automated and accurate external camera calibration method for 3D surgical scene reconstruction (3D-SSR)<n>We contribute a novel, fast, and fully automatic calibration method based on the projection of multi-scale markers (MSMs) using a ceiling-mounted projector.
  arXiv  Detail & Related papers  (2025-01-27T17:10:33Z)
• 3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction [50.07071392673984]
  Existing methods learn 3D rotations parametrized in the spatial domain using angles or quaternions. We propose a frequency-domain approach that directly predicts Wigner-D coefficients for 3D rotation regression. Our method achieves state-of-the-art results on benchmarks such as ModelNet10-SO(3) and PASCAL3D+.
  arXiv  Detail & Related papers  (2024-11-01T12:50:38Z)
• CRiM-GS: Continuous Rigid Motion-Aware Gaussian Splatting from Motion-Blurred Images [14.738528284246545]
  CRiM-GS is a textbfContinuous textbfRigid textbfMotion-aware textbfGaussian textbfSplatting.<n>It reconstructs precise 3D scenes from motion-blurred images while maintaining real-time rendering speed.
  arXiv  Detail & Related papers  (2024-07-04T13:37:04Z)
• iComMa: Inverting 3D Gaussian Splatting for Camera Pose Estimation via Comparing and Matching [14.737266480464156]
  We present a method named iComMa to address the 6D camera pose estimation problem in computer vision. We propose an efficient method for accurate camera pose estimation by inverting 3D Gaussian Splatting (3DGS)
  arXiv  Detail & Related papers  (2023-12-14T15:31:33Z)
• MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision [72.5863451123577]
  We show how to train a neural model that can perform accurate 3D pose and camera estimation. Our method outperforms both classical bundle adjustment and weakly-supervised monocular 3D baselines.
  arXiv  Detail & Related papers  (2021-08-10T18:39:56Z)
• CoMo: A novel co-moving 3D camera system [0.0]
  CoMo is a co-moving camera system of two synchronized high speed cameras coupled with rotational stages. We address the calibration of the external parameters measuring the position of the cameras and their three angles of yaw, pitch and roll in the system "home" configuration. We evaluate the robustness and accuracy of the system by comparing reconstructed and measured 3D distances in what we call 3D tests, which show a relative error of the order of 1%.
  arXiv  Detail & Related papers  (2021-01-26T13:29:13Z)
• Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
  Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step. This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
  arXiv  Detail & Related papers  (2020-08-31T17:10:48Z)
• Spatiotemporal Bundle Adjustment for Dynamic 3D Human Reconstruction in the Wild [49.672487902268706]
  We present a framework that jointly estimates camera temporal alignment and 3D point triangulation. We reconstruct 3D motion trajectories of human bodies in events captured by multiple unsynchronized and unsynchronized video cameras.
  arXiv  Detail & Related papers  (2020-07-24T23:50:46Z)
• Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation [57.11299763566534]
  We present a solution to recover 3D pose from multi-view images captured with spatially calibrated cameras. We exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points. Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections.
  arXiv  Detail & Related papers  (2020-04-05T12:52:29Z)

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.