Pros of openMVG

• Lightweight and modular design, making it easier to integrate into existing projects
• Extensive documentation and tutorials for users and developers
• Strong focus on accuracy and mathematical correctness in implementations

Cons of openMVG

• Less feature-rich compared to AliceVision, especially in terms of advanced reconstruction techniques
• Smaller community and slower development pace
• Limited support for GPU acceleration

Code Comparison

AliceVision example (feature extraction):

#include <aliceVision/feature/feature.hpp>
#include <aliceVision/image/image.hpp>

aliceVision::image::Image<float> image;
std::unique_ptr<aliceVision::feature::Regions> regions;
aliceVision::feature::SIFT_Anatomy_Image_Describer_CUDA sift_describer;
sift_describer.Describe(image, regions);

openMVG example (feature extraction):

#include "openMVG/image/image.hpp"
#include "openMVG/features/sift/SIFT_Anatomy_Image_Describer.hpp"

openMVG::image::Image<unsigned char> image;
std::unique_ptr<openMVG::features::Regions> regions;
openMVG::features::SIFT_Anatomy_Image_Describer sift_describer;
sift_describer.Describe(image, regions);

Both libraries offer similar functionality for feature extraction, but AliceVision provides CUDA support for improved performance on compatible hardware.

Pros of COLMAP

• More extensive documentation and tutorials
• Faster processing times for large datasets
• Better support for GPU acceleration

Cons of COLMAP

• Less user-friendly interface
• Fewer built-in features for mesh generation and texturing
• Limited support for video input

Code Comparison

AliceVision:

#include <aliceVision/sfmData/SfMData.hpp>
#include <aliceVision/sfmDataIO/sfmDataIO.hpp>

aliceVision::sfmData::SfMData sfmData;
aliceVision::sfmDataIO::Load(sfmData, sfmDataFilename, aliceVision::sfmDataIO::ESfMData::ALL);

COLMAP:

#include <colmap/base/reconstruction.h>
#include <colmap/util/string.h>

colmap::Reconstruction reconstruction;
reconstruction.ReadText(colmap::JoinPaths(input_path, "cameras.txt"),
                        colmap::JoinPaths(input_path, "images.txt"),
                        colmap::JoinPaths(input_path, "points3D.txt"));

Both AliceVision and COLMAP are powerful open-source photogrammetry libraries. AliceVision offers a more comprehensive pipeline with built-in meshing and texturing capabilities, while COLMAP excels in performance and scalability for large datasets. COLMAP's code tends to be more low-level and requires more manual setup, whereas AliceVision provides higher-level abstractions for easier integration into existing workflows.

Pros of OpenSfM

• Lightweight and more focused on street-level imagery processing
• Better integration with OpenStreetMap data
• More straightforward installation process

Cons of OpenSfM

• Less comprehensive feature set compared to AliceVision
• Smaller community and fewer updates
• Limited to Python, while AliceVision offers C++ implementation

Code Comparison

OpenSfM example:

import opensfm
reconstruction = opensfm.Reconstruction()
reconstruction.add_camera('camera1', 'perspective', [800, 600])
reconstruction.add_shot('shot1', 'camera1', [0, 0, 0], [0, 0, 0])

AliceVision example:

#include <aliceVision/sfmData/SfMData.hpp>
aliceVision::sfmData::SfMData sfmData;
sfmData.views[0] = std::make_shared<aliceVision::sfmData::View>("image.jpg", 0, 0);
sfmData.intrinsics[0] = std::make_shared<aliceVision::camera::Pinhole>(1000, 1000, 36.0);

Both libraries offer functionality for Structure from Motion (SfM) and 3D reconstruction, but AliceVision provides a more comprehensive toolset with additional features like photogrammetry and mesh generation. OpenSfM is more specialized for street-level imagery and has better integration with mapping services. The code examples show that OpenSfM uses a Python interface, making it more accessible for rapid prototyping, while AliceVision's C++ implementation may offer better performance for large-scale projects.

Pros of PCL

• Extensive library for 3D point cloud processing with a wide range of algorithms
• Large and active community, providing support and continuous development
• Well-documented with numerous tutorials and examples

Cons of PCL

• Steeper learning curve due to its comprehensive nature
• Can be computationally intensive for large datasets
• Primarily focused on point cloud processing, less suitable for general computer vision tasks

Code Comparison

PCL (Point Cloud Processing):

#include <pcl/point_types.h>
#include <pcl/filters/passthrough.h>

pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PassThrough<pcl::PointXYZ> pass;
pass.setInputCloud(cloud);
pass.setFilterFieldName("z");
pass.setFilterLimits(0.0, 1.0);

AliceVision (Structure from Motion):

#include <aliceVision/sfmData/SfMData.hpp>
#include <aliceVision/sfmDataIO/sfmDataIO.hpp>

aliceVision::sfmData::SfMData sfmData;
aliceVision::sfmDataIO::Load(sfmData, sfmDataFilename, aliceVision::sfmDataIO::ESfMData::ALL);

Pros of OpenCV

• Broader scope: Covers a wide range of computer vision tasks beyond photogrammetry
• Larger community: More extensive documentation, tutorials, and third-party resources
• Cross-platform support: Works on multiple operating systems and devices

Cons of OpenCV

• Less specialized: Not as focused on photogrammetry and 3D reconstruction
• Steeper learning curve: Due to its extensive feature set, it can be overwhelming for beginners

Code Comparison

AliceVision (SfM pipeline):

sfmData::SfMData sfmData;
sfm::SfMAlgorithms sfmAlgorithms;
sfm::ReconstructionEngine_sequentialSfM sfmEngine(
    sfmData,
    sfmAlgorithms,
    stlplus::folder_append_separator(outDirectory) + "sequential_reconstruction");

OpenCV (Feature detection and matching):

cv::Ptr<cv::Feature2D> detector = cv::xfeatures2d::SIFT::create();
std::vector<cv::KeyPoint> keypoints;
cv::Mat descriptors;
detector->detectAndCompute(image, cv::noArray(), keypoints, descriptors);

Both libraries offer powerful tools for computer vision tasks, but AliceVision is more specialized in photogrammetry and 3D reconstruction, while OpenCV provides a broader range of functionalities for various computer vision applications.