奥比中光相机的深度图像数据（TUM数据集）

https://github.com/ParikaGoel/KinectFusion

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ismar2011.pdf

https://vision.in.tum.de/data/datasets/rgbd-dataset/download

https://dev.intelrealsense.com/docs

https://github.com/IntelRealSense/librealsense/releases

https://www.mrpt.org/tutorials/programming/maths-and-geometry/2d_3d_geometry/

https://svncvpr.in.tum.de/cvpr-ros-pkg/trunk/rgbd_benchmark/rgbd_benchmark_tools/src/rgbd_benchmark_tools/

import argparseimport sysimport osimport numpy

def read_file_list(filename):    """    Reads a trajectory from a text file. 
    File format:    The file format is "stamp d1 d2 d3 ...", where stamp denotes the time stamp (to be matched)    and "d1 d2 d3.." is arbitary data (e.g., a 3D position and 3D orientation) associated to this timestamp. 
    Input:    filename -- File name
    Output:    dict -- dictionary of (stamp,data) tuples
    """    file = open(filename)    data = file.read()    lines = data.replace(",", " ").replace("\t", " ").split("\n")    list = [[v.strip() for v in line.split(" ") if v.strip() != ""]            for line in lines if len(line) > 0 and line[0] != "#"]    list = [(float(l[0]), l[1:]) for l in list if len(l) > 1]    return dict(list)

def associate(first_list, second_list, offset, max_difference):    """    Associate two dictionaries of (stamp,data). As the time stamps never match exactly, we aim     to find the closest match for every input tuple.
    Input:    first_list -- first dictionary of (stamp,data) tuples    second_list -- second dictionary of (stamp,data) tuples    offset -- time offset between both dictionaries (e.g., to model the delay between the sensors)    max_difference -- search radius for candidate generation
    Output:    matches -- list of matched tuples ((stamp1,data1),(stamp2,data2))
    """    first_keys = first_list.keys()    second_keys = second_list.keys()    potential_matches = [(abs(a - (b + offset)), a, b)                         for a in first_keys                         for b in second_keys                         if abs(a - (b + offset)) < max_difference]    potential_matches.sort()    matches = []    for diff, a, b in potential_matches:        if a in first_keys and b in second_keys:            first_keys.remove(a)            second_keys.remove(b)            matches.append((a, b))
    matches.sort()    return matches

if __name__ == '__main__':
    # parse command line    parser = argparse.ArgumentParser(description='''    This script takes two data files with timestamps and associates them       ''')    parser.add_argument(        'first_file', help='first text file (format: timestamp data)')    parser.add_argument(        'second_file', help='second text file (format: timestamp data)')    parser.add_argument(        '--first_only', help='only output associated lines from first file', action='store_true')    parser.add_argument(        '--offset', help='time offset added to the timestamps of the second file (default: 0.0)', default=0.0)    parser.add_argument(        '--max_difference', help='maximally allowed time difference for matching entries (default: 0.02)', default=0.02)    args = parser.parse_args()
    first_list = read_file_list(args.first_file)    second_list = read_file_list(args.second_file)
    matches = associate(first_list, second_list, float(        args.offset), float(args.max_difference))
    if args.first_only:        for a, b in matches:            print("%f %s" % (a, " ".join(first_list[a])))    else:        for a, b in matches:            print("%f %s %f %s" % (a, " ".join(                first_list[a]), b-float(args.offset), " ".join(second_list[b])))

https://svncvpr.in.tum.de/cvpr-ros-pkg/trunk/rgbd_benchmark/rgbd_benchmark_tools/src/rgbd_benchmark_tools/

https://vision.in.tum.de/data/datasets/rgbd-dataset/tools#evaluation

  1024×768×16bit =   (1024×768×16)/8 Byte =   [(1024×768×16)/8]/1024 KB =   1536 KB =   {[(1024×768×16)/8]/1024}/1024 MB   = 1.5 MB

focalLength = 525.0centerX = 319.5centerY = 239.5scalingFactor = 5000.0

def depth2mi(depthValue):
    return depthValue * 0.001

def depth2xyz(u, v, depthValue):    fx = 361.1027    fy = 361.8266    cx = 258.4545    cy = 212.1282
    depth = depth2mi(depthValue)
    z = float(depth)    x = float((u - cx) * z) / fx    y = float((v - cy) * z) / fy
    result = [x, y, z]    return result

#深度图存入np数组arr = np.array(depth)#彩色图上某点像素坐标（u，v）在对齐深度图的深度值depthValue =float(arr3[v, u])coordinate = depth2xyz(u, v, depthValue)