KinectFusion

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.

Features

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

Dependencies

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.

Prerequisites

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")

Usage

Cpp
#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);