Sparse 3D Reconstruction and Bundle Adjustment | Saikiran Juttu

This project implements a full Structure from Motion (SfM) pipeline on a Buddha statue using a sequence of 24 grayscale images. It combines feature detection, epipolar geometry, camera pose recovery, triangulation, and bundle adjustment.

Dataset

24 images of a wooden Buddha statue captured at different angles
Enhanced using CLAHE (Contrast Limited Adaptive Histogram Equalization)
Features extracted using SIFT with custom parameters

Pipeline Overview

1. Image Preprocessing

Using CLAHE improves contrast on low-texture surfaces like carved wood.

2. SIFT Feature Detection

Applied to all 24 images
Used BFMatcher with ratio test
Matches filtered via RANSAC for outlier rejection

3. Essential Matrix & Pose Recovery

Computed Essential matrix using calibrated camera matrix
Used cv2.recoverPose() to derive relative rotation and translation between views
Built a chain of camera poses from image 0 onward

4. Triangulation

3D points computed from pixel correspondences using cv2.triangulatePoints()
All 3D points stored in homogeneous form
Colored and visualized using Plotly

5. Bundle Adjustment with GTSAM

Built a factor graph with:
- Camera pose priors
- Between factors from pose transitions
- Projection factors from 2D-3D matches
Used Levenberg-MarquardtOptimizer for refinement

Results

Initial 3D Trajectory

Optimized 3D Trajectory after Bundle Adjustment

Average reprojection error reduced by ~15%
Landmark cloud tightened around object geometry
Rotation drift corrected with global optimization

Tools & Libraries

OpenCV (SIFT, RANSAC, triangulation)
NumPy, Matplotlib, Plotly
GTSAM (factor graph + BA)
Python

Resources

🔗 GitHub Repo

This project demonstrates a scalable pipeline for SfM using minimal dependencies. It serves as a foundation for integrating real-time VIO or stereo SLAM on embedded platforms.