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What This Project Achieves
This project investigates how reliable 3D reconstruction can be achieved from standard consumer cameras using COLMAP, a structure-from-motion system widely used in robotics and AR/VR pipelines. By conducting controlled experiments with varying image counts, angles, lighting, texture conditions, and COLMAP parameters, the study identifies which factors most strongly influence reconstruction quality. The results provide practical guidelines for building stable 3D maps—critical for mobile robot perception, navigation, and manipulation tasks.
How We Built It — Key Highlights
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Captured datasets with varying image counts, camera angles, and scene consistency to observe reconstruction changes.
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Used COLMAP’s full pipeline—feature extraction, matching, geometric verification, and bundle adjustment—to generate sparse 3D models.
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Experimented with hyperparameters such as peak_threshold and edge_threshold, discovering combinations that extracted more stable features. (Final presentation slide shows optimal values: peak_threshold = 0.02, edge_threshold = 5.)
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Demonstrated that scene consistency and feature quality matter more than raw image quantity for reconstruction accuracy.
Challenges
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Moving objects in the scene caused inconsistent feature matches, leading to unstable reconstructions. (Evident in initial experiments highlighted in PL feedback.)
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Differences in lighting, focus, and viewpoint created feature sparsity, reducing COLMAP’s ability to triangulate reliably.
Insights
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Strict feature filtering and well-chosen thresholds significantly improved reconstruction clarity.
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High-quality, consistent images with stable viewpoints improved results more than simply collecting more data.
Project Reflection
This PBL gave me valuable hands-on experience—from camera calibration to full 3D reconstruction—and helped me understand how factors like lighting, viewpoint, and focus impact algorithm performance. Through refining my dataset and troubleshooting unexpected results, I learned how to improve reconstruction quality and guide the research direction of our final project.
Visual Highlights
Reviewer Feedback
Zhihao demonstrated strong technical depth in applying the full COLMAP reconstruction pipeline, from data collection to parameter tuning and detailed result analysis. His disciplined experimentation and ability to diagnose issues—such as object motion and feature instability—show a mature understanding of 3D reconstruction challenges. He also led team coordination and research direction, contributing significantly to the project’s clarity, rigor, and overall success.