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

TL;DR

The paper addresses the challenge of accurate camera path estimation for high-fidelity 3D modeling by introducing a modified ASIFT that increases feature matches with reduced overhead, combined with a two-camera rotation correction and stereo translation estimation within a Structure-from-Motion framework. The proposed approach yields a highly accurate camera trajectory (reported ~99.9% ground-truth alignment) and improves 3D reconstruction quality over state-of-the-art methods. Key contributions include the modified ASIFT feature strategy, dual-camera rotation averaging, stereo-based translation correction, and an end-to-end workflow for generating precise 3D models. The work has practical implications for efficient, robust 3D reconstruction in applications requiring high fidelity and reliable camera localization.

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.

Figures (7)

• Figure 1: Comparison of SIFT and ASIFT match points
• Figure 2: Comparison of number of Inliers using SIFT and modified ASIFT
• Figure 3: Stereo camera positioning model
• Figure 4: Proposed model
• Figure 5: Actual camera movement path and generated camera movement path using proposed scheme