Structure from Motion (SfM) Pipeline

The run_sfm function implements a Structure from Motion (SfM) pipeline for reconstructing 3D structures from image datasets. It supports multiple options for visualization, logging, and dataset control, making it suitable for educational, research, and development purposes.

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Function Overview

The run_sfm function processes a specific dataset and performs the following tasks:

1. Feature extraction using SIFT.
2. Estimation of relative camera poses using robust methods like RANSAC.
3. Triangulation of 3D points.
4. Optimization and reconstruction of absolute camera poses and 3D points.
5. **Visualization of 3D structures and camera poses.

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Function Signature

function [] = run_sfm(dataset_num, varargin)

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How to Run the Code

Follow the steps below to successfully execute the run_sfm function and process a dataset for Structure from Motion (SfM):

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1. Prerequisites

Before running the code, ensure you have the following:

• MATLAB: Installed and properly set up.
• VLFeat Toolbox:
  • Download the toolbox from VLFeat Website.
  • Extract it and make sure its path is correctly added to your MATLAB workspace.
  • Example: Ensure the vl_setup script from VLFeat is accessible.

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2. Setup

1. Add the Required Paths:

   • Ensure all necessary files and functions (e.g., feature_extraction, estimate_E_robust, Cheirality_triangulate) are in your MATLAB path.
   • Run the vl_setup script to initialize VLFeat.

2. Prepare the Dataset:

   • Ensure the dataset files (e.g., images and camera calibration data) are in the required format and location.
   • Each dataset must be supported by the get_dataset_info function, which extracts intrinsic camera parameters and image names.

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3. Basic Execution

Run the run_sfm function in MATLAB using the following syntax:

run_sfm(dataset_num);

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Sample of the results: