BEST OUTCOME
Reconstructing 3D Spaces Using COLMAP: Understanding What Makes Reconstruction Succeed or Fail
A systematic exploration of how image quality, camera movement, and algorithm parameters shape 3D reconstruction accuracy.
DEMONSTRATED CAPABILITY
System-Level Perception Design
🎓 Graduate-level Systems Thinking
Designed and evaluated an interactive 3D reconstruction system by balancing neural rendering fidelity, geometric consistency, and hardware constraints to support scalable AR/VR experiences.
Analyzed and compared alternative reconstruction approaches to select methods that balance visual quality, computational efficiency, and interactivity under constrained resources.
Compared alternative technical approaches by analyzing trade-offs in accuracy, robustness, and resource constraints to inform system-level design decisions.
Compared alternative technical approaches by analyzing trade-offs in accuracy, robustness, and resource constraints to inform system-level design decisions.
What This Project Achieves
This project explores how to build reliable 3D models of real-world scenes using an ordinary camera and open-source reconstruction tools. By running controlled experiments with different numbers of images, viewing angles, lighting conditions, and parameter settings, it shows which factors matter most for getting a good 3D result. The findings offer practical guidelines that can help mobile robots better understand their surroundings for navigation and manipulation tasks.
How This Was Built — Key Highlights
This project used an iterative, experiment-driven process to understand how everyday camera images can be turned into accurate 3D reconstructions. The approach focused on collecting varied data, adjusting reconstruction settings, and analyzing how these changes impacted the final 3D output.
Collected different sets of photos with varied image counts, viewing angles, and scene stability to compare reconstruction results.
Used COLMAP’s full reconstruction workflow to extract features, match them across images, and build a 3D model of the scene.
Adjusted key parameters—including peak_threshold and edge_threshold—to identify settings that produced cleaner, more consistent features.
Observed that image quality and scene consistency had a larger impact on reconstruction accuracy than simply increasing the number of photos.
Challenges
This project faced several practical issues related to image quality and scene conditions, which affected the reliability of the 3D reconstruction process. These challenges highlight how sensitive reconstruction tools can be to movement, lighting, and camera positioning.
Moving objects in the scene introduced inconsistent features, leading to unstable or incomplete reconstructions.
Variations in lighting, focus, and viewpoint reduced the number of usable features the system could detect across images.
Minor changes between photos made it difficult for the reconstruction tool to match points accurately.
Insights
Analysis of the experiment results revealed several factors that consistently improved the quality of the 3D models. These insights point to practical guidelines for collecting data that leads to more stable reconstructions.
Applying stricter feature filtering and well-chosen parameter settings produced cleaner and more reliable reconstruction outputs.
Capturing high-quality images with consistent lighting and steady viewpoints improved results more than simply increasing the number of photos.
Careful control of the scene—keeping objects stationary and backgrounds consistent—had a significant impact on reconstruction stability.
Project Gallery
Academic Team Feedback
The academic team noted that this project applied a careful and methodical approach to 3D reconstruction, supported by clear experimentation and well-reasoned analysis. The contributor demonstrated strong understanding of how scene conditions, image quality, and parameter choices affect reconstruction stability. Feedback from the Project Lead—a researcher at MIT CSAIL and co-founder of XYZ Robotics—highlighted this work as a thoughtful and well-executed application of reconstruction principles.
Project Reflection
This PBL gave me hands-on experience in building 3D reconstructions and helped me understand how factors like lighting, viewpoint, and image consistency affect the results. By refining my dataset and interpreting unexpected outcomes, I learned how to improve reconstruction quality and make more informed decisions throughout the project.