KinectFusionLib - Modern Implementation of the KinectFusion Approach

1Technical University of Munich


Implementation of the KinectFusion approach to generating three-dimensional models from depth image scans.

Here, the original method has been extended with the MarchingCubes algorithm to allow exporting the model as a dense surface mesh.

Realized in modern C++14 and CUDA to allow real-time reconstruction.

Developed in the context of an interdisciplinary project in cooperation with the Chair for Computer Aided Medical Procedures and Augmented Reality and Dynamify GmbH.


This is an implementation of KinectFusion, based on Newcombe, Richard A., et al. KinectFusion: Real-time dense surface mapping and tracking. It makes heavy use of graphics hardware and thus allows for real-time fusion of depth image scans. Furthermore, exporting of the resulting fused volume is possible either as a pointcloud or a dense surface mesh.


  • Real-time fusion of depth scans and corresponding RGB color images
  • Easy to use, modern C++14 interface
  • Export of the resulting volume as pointcloud
  • Export also as dense surface mesh using the MarchingCubes algorithm
  • Functions for easy export of pointclouds and meshes into the PLY file format
  • Retrieval of calculated camera poses for further processing


  • GCC 5 as higher versions do not work with current nvcc (as of 2017).
  • CUDA 8.0. In order to provide real-time reconstruction, this library relies on graphics hardware. Running it exclusively on the CPU is not possible.
  • OpenCV 3.0 or higher. This library heavily depends on the GPU features of OpenCV that have been refactored in the 3.0 release. Therefore, OpenCV 2 is not supported.
  • Eigen3 for efficient matrix and vector operations.


  • Adjust CUDA architecture: Set the CUDA architecture version to that of your graphics hardware SET(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-O3 -gencode arch=compute_52,code=sm_52) Tested with a nVidia GeForce 970, compute capability 5.2, Maxwell architecture
  • Set custom opencv path (if necessary): SET("OpenCV_DIR" "/opt/opencv/usr/local/share/OpenCV")


#include <kinectfusion.h>

// Define the data source
XtionCamera camera {};

// Get a global configuration (comes with default values) and adjust some parameters
kinectfusion::GlobalConfiguration configuration;
configuration.voxel_scale = 2.f;
configuration.init_depth = 700.f;
configuration.distance_threshold = 10.f;
configuration.angle_threshold = 20.f;

// Create a KinectFusion pipeline with the camera intrinsics and the global configuration
kinectfusion::Pipeline pipeline { camera.get_parameters(), configuration };

// Then, just loop over the incoming frames
while ( !end ) {
    // 1) Grab a frame from the data source
    InputFrame frame = camera.grab_frame();

    // 2) Have the pipeline fuse it into the global volume
    bool success = pipeline.process_frame(frame.depth_map, frame.color_map);
    if (!success)
        std::cout << "Frame could not be processed" << std::endl;

// Retrieve camera poses
auto poses = pipeline.get_poses();

// Export surface mesh
auto mesh = pipeline.extract_mesh();
kinectfusion::export_ply("data/mesh.ply", mesh);

// Export pointcloud
auto pointcloud = pipeline.extract_pointcloud();
kinectfusion::export_ply("data/pointcloud.ply", pointcloud);

For a more in-depth example and implementations of the data sources, have a look at the KinectFusionApp.


This library is licensed under MIT.