Question 1

How to get started with 3D reconstruction from images as a beginner?

Accepted Answer

Start with the 'Tutorials' section, which includes introductory materials on SLAM, SfM, and MVS, such as the 'All in one tutorial' by Yu Huang. Then, explore open-source software like COLMAP or OpenMVG listed in the 'OpenSource' section for hands-on practice.

Question 2

What are the best datasets for evaluating multi-view stereo algorithms?

Accepted Answer

The 'Datasets' section lists ground-truth datasets like Middlebury Multi-view Stereo, DTU MVS Data Set, and Tanks and Temples, which are widely used for benchmarking. Choose based on your needs: Middlebury for controlled environments, DTU for varied lighting, and Tanks and Temples for large-scale scenes.

Question 3

Is COLMAP or OpenMVG better for structure-from-motion?

Accepted Answer

Both are listed in the 'OpenSource SfM' table: COLMAP is BSD-licensed and supports CUDA for MVS, while OpenMVG is MPL2-licensed and focuses on incremental SfM. COLMAP is more comprehensive with MVS integration, but OpenMVG is lighter and permissive; choose based on license preferences and feature needs.

Question 4

How does machine learning improve multi-view stereo compared to traditional methods?

Accepted Answer

The 'Machine Learning MVS' subsection papers, like MVSNet and DeepMVS, show that learning-based approaches can handle textureless regions and noisy data better by leveraging neural networks. However, they require large datasets and computational resources, unlike classical methods listed in 'Point cloud computation'.

Question 5

Are there any real-time SLAM libraries that work with Python?

Accepted Answer

The 'OpenSource SLAM' table lists C++/ROS-based libraries like ORB-SLAM and SVO, but Python options aren't highlighted. For Python, you might need to look beyond this list at tools like OpenCV's SLAM modules or PySLAM, as this list focuses on established academic implementations.

Question 6

What's the difference between incremental and global SfM?

Accepted Answer

The papers are categorized under 'Incremental SfM' (e.g., Photo Tourism) and 'Global SfM' (e.g., Global Fusion of Relative Motions). Incremental methods add cameras sequentially and are prone to drift, while global methods optimize all cameras simultaneously for better accuracy but require more computation.

Awesome 3D reconstruction list

What is Awesome 3D reconstruction list?

Overview

Use Cases

Best For

Found a gem we're missing?

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions