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MVSBoost: An Efficient Point Cloud-based 3D Reconstruction

Umair Haroon, Ahmad AlMughrabi, Ricardo Marques, Petia Radeva

TL;DR

This work targets the efficiency and accuracy gap between traditional MVS and neural implicit field methods for 3D reconstruction. It introduces MVSBoost, a pipeline that combines structure-from-motion pose estimation on multi-view 360-degree imagery with point-cloud densification, mesh reconstruction, refinement, and texture mapping to produce high-fidelity 3D models. Through Chamfer-distance evaluations on Realistic Synthetic 360, MVSBoost demonstrates superior accuracy and computational efficiency, outperforming several neural implicit field baselines while robustly handling occlusions and varying viewpoints. The approach offers practical implications for real-time, scalable 3D reconstruction in AR/VR, medical imaging, and media production, with future work focusing on further speedups and applicability to more challenging lighting and reflective environments.

Abstract

Efficient and accurate 3D reconstruction is crucial for various applications, including augmented and virtual reality, medical imaging, and cinematic special effects. While traditional Multi-View Stereo (MVS) systems have been fundamental in these applications, using neural implicit fields in implicit 3D scene modeling has introduced new possibilities for handling complex topologies and continuous surfaces. However, neural implicit fields often suffer from computational inefficiencies, overfitting, and heavy reliance on data quality, limiting their practical use. This paper presents an enhanced MVS framework that integrates multi-view 360-degree imagery with robust camera pose estimation via Structure from Motion (SfM) and advanced image processing for point cloud densification, mesh reconstruction, and texturing. Our approach significantly improves upon traditional MVS methods, offering superior accuracy and precision as validated using Chamfer distance metrics on the Realistic Synthetic 360 dataset. The developed MVS technique enhances the detail and clarity of 3D reconstructions and demonstrates superior computational efficiency and robustness in complex scene reconstruction, effectively handling occlusions and varying viewpoints. These improvements suggest that our MVS framework can compete with and potentially exceed current state-of-the-art neural implicit field methods, especially in scenarios requiring real-time processing and scalability.

MVSBoost: An Efficient Point Cloud-based 3D Reconstruction

TL;DR

This work targets the efficiency and accuracy gap between traditional MVS and neural implicit field methods for 3D reconstruction. It introduces MVSBoost, a pipeline that combines structure-from-motion pose estimation on multi-view 360-degree imagery with point-cloud densification, mesh reconstruction, refinement, and texture mapping to produce high-fidelity 3D models. Through Chamfer-distance evaluations on Realistic Synthetic 360, MVSBoost demonstrates superior accuracy and computational efficiency, outperforming several neural implicit field baselines while robustly handling occlusions and varying viewpoints. The approach offers practical implications for real-time, scalable 3D reconstruction in AR/VR, medical imaging, and media production, with future work focusing on further speedups and applicability to more challenging lighting and reflective environments.

Abstract

Efficient and accurate 3D reconstruction is crucial for various applications, including augmented and virtual reality, medical imaging, and cinematic special effects. While traditional Multi-View Stereo (MVS) systems have been fundamental in these applications, using neural implicit fields in implicit 3D scene modeling has introduced new possibilities for handling complex topologies and continuous surfaces. However, neural implicit fields often suffer from computational inefficiencies, overfitting, and heavy reliance on data quality, limiting their practical use. This paper presents an enhanced MVS framework that integrates multi-view 360-degree imagery with robust camera pose estimation via Structure from Motion (SfM) and advanced image processing for point cloud densification, mesh reconstruction, and texturing. Our approach significantly improves upon traditional MVS methods, offering superior accuracy and precision as validated using Chamfer distance metrics on the Realistic Synthetic 360 dataset. The developed MVS technique enhances the detail and clarity of 3D reconstructions and demonstrates superior computational efficiency and robustness in complex scene reconstruction, effectively handling occlusions and varying viewpoints. These improvements suggest that our MVS framework can compete with and potentially exceed current state-of-the-art neural implicit field methods, especially in scenarios requiring real-time processing and scalability.
Paper Structure (15 sections, 4 figures, 1 table)

This paper contains 15 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Neural Implicit Fields
  4. Proposed Methodology
  5. Overview
  6. Our Proposal: MVSBoost
  7. Camera Pose and Point Cloud Estimation
  8. 3D Reconstruction
  9. Experimental Results
  10. Evaluation protocol
  11. Implementation settings
  12. Datasets
  13. Comparative Analysis
  14. Discussion
  15. Conclusions and Future Work

Figures (4)

  • Figure 1: This figure illustrates the proposed multi-phases framework, where (a) takes multi-view 360-degree images with transparent background (i.e; object-centric RGBA images); pass them to (b) Camera Pose and Point Cloud Estimation phase, which estimates a point cloud and camera poses based on the extracted SIFT features from RGBA inputs; the point cloud passed to (c) Densify Point Cloud for obtaining a complete and accurate as possible point-cloud; (d) Mesh Reconstruction for estimating a mesh surface that explains the best the input point-cloud; (e) Mesh Refinement for recovering all fine details; (f) Texture Mesh for computing a sharp and accurate texture to color the mesh; finally (j) is the output mesh.
  • Figure 2: An illustration on the 3D reconstruction module on the Chair scene.
  • Figure 3: A side-by-side comparison of our method's results with the ground truth from the Realistic Synthetic 360 dataset. The figure illustrates significant differences and improvements in the presented scenes: the Chair, Hotdog, and Ship. Our reconstruction exhibits superior precision, refinement, detail, and texture fidelity compared to existing methods, as evidenced by the clear visual distinctions.
  • Figure 4: A detailed comparison showcasing selected mesh parts from ground truth, our method, and previous state-of-the-art methods, demonstrating robustness by preserving a high level of details in challenging areas where previous methods struggle, as highlighted in this figure.