diff --git a/poses/README.md b/poses/README.md
new file mode 100644
index 0000000..3f2a572
--- /dev/null
+++ b/poses/README.md
@@ -0,0 +1,2 @@
+Contains the COLMAP wrappers and other utils to extract, read and write camera
+poses from and to different formats.
diff --git a/poses/__init__.py b/poses/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/poses/colmap_read_model.py b/poses/colmap_read_model.py
new file mode 100644
index 0000000..4acaa8c
--- /dev/null
+++ b/poses/colmap_read_model.py
@@ -0,0 +1,312 @@
+# Copyright (c) 2018, ETH Zurich and UNC Chapel Hill.
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+#     * Redistributions of source code must retain the above copyright
+#       notice, this list of conditions and the following disclaimer.
+#
+#     * Redistributions in binary form must reproduce the above copyright
+#       notice, this list of conditions and the following disclaimer in the
+#       documentation and/or other materials provided with the distribution.
+#
+#     * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
+#       its contributors may be used to endorse or promote products derived
+#       from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+#
+# Author: Johannes L. Schoenberger (jsch at inf.ethz.ch)
+
+import os
+import sys
+import collections
+import numpy as np
+import struct
+
+
+CameraModel = collections.namedtuple(
+    "CameraModel", ["model_id", "model_name", "num_params"])
+Camera = collections.namedtuple(
+    "Camera", ["id", "model", "width", "height", "params"])
+BaseImage = collections.namedtuple(
+    "Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"])
+Point3D = collections.namedtuple(
+    "Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"])
+
+class Image(BaseImage):
+    def qvec2rotmat(self):
+        return qvec2rotmat(self.qvec)
+
+
+CAMERA_MODELS = {
+    CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3),
+    CameraModel(model_id=1, model_name="PINHOLE", num_params=4),
+    CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4),
+    CameraModel(model_id=3, model_name="RADIAL", num_params=5),
+    CameraModel(model_id=4, model_name="OPENCV", num_params=8),
+    CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8),
+    CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12),
+    CameraModel(model_id=7, model_name="FOV", num_params=5),
+    CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4),
+    CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5),
+    CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12)
+}
+CAMERA_MODEL_IDS = dict([(camera_model.model_id, camera_model) \
+                         for camera_model in CAMERA_MODELS])
+
+
+def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"):
+    """Read and unpack the next bytes from a binary file.
+    :param fid:
+    :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc.
+    :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
+    :param endian_character: Any of {@, =, <, >, !}
+    :return: Tuple of read and unpacked values.
+    """
+    data = fid.read(num_bytes)
+    return struct.unpack(endian_character + format_char_sequence, data)
+
+
+def read_cameras_text(path):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::WriteCamerasText(const std::string& path)
+        void Reconstruction::ReadCamerasText(const std::string& path)
+    """
+    cameras = {}
+    with open(path, "r") as fid:
+        while True:
+            line = fid.readline()
+            if not line:
+                break
+            line = line.strip()
+            if len(line) > 0 and line[0] != "#":
+                elems = line.split()
+                camera_id = int(elems[0])
+                model = elems[1]
+                width = int(elems[2])
+                height = int(elems[3])
+                params = np.array(tuple(map(float, elems[4:])))
+                cameras[camera_id] = Camera(id=camera_id, model=model,
+                                            width=width, height=height,
+                                            params=params)
+    return cameras
+
+
+def read_cameras_binary(path_to_model_file):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::WriteCamerasBinary(const std::string& path)
+        void Reconstruction::ReadCamerasBinary(const std::string& path)
+    """
+    cameras = {}
+    with open(path_to_model_file, "rb") as fid:
+        num_cameras = read_next_bytes(fid, 8, "Q")[0]
+        for camera_line_index in range(num_cameras):
+            camera_properties = read_next_bytes(
+                fid, num_bytes=24, format_char_sequence="iiQQ")
+            camera_id = camera_properties[0]
+            model_id = camera_properties[1]
+            model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name
+            width = camera_properties[2]
+            height = camera_properties[3]
+            num_params = CAMERA_MODEL_IDS[model_id].num_params
+            params = read_next_bytes(fid, num_bytes=8*num_params,
+                                     format_char_sequence="d"*num_params)
+            cameras[camera_id] = Camera(id=camera_id,
+                                        model=model_name,
+                                        width=width,
+                                        height=height,
+                                        params=np.array(params))
+        assert len(cameras) == num_cameras
+    return cameras
+
+
+def read_images_text(path):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::ReadImagesText(const std::string& path)
+        void Reconstruction::WriteImagesText(const std::string& path)
+    """
+    images = {}
+    with open(path, "r") as fid:
+        while True:
+            line = fid.readline()
+            if not line:
+                break
+            line = line.strip()
+            if len(line) > 0 and line[0] != "#":
+                elems = line.split()
+                image_id = int(elems[0])
+                qvec = np.array(tuple(map(float, elems[1:5])))
+                tvec = np.array(tuple(map(float, elems[5:8])))
+                camera_id = int(elems[8])
+                image_name = elems[9]
+                elems = fid.readline().split()
+                xys = np.column_stack([tuple(map(float, elems[0::3])),
+                                       tuple(map(float, elems[1::3]))])
+                point3D_ids = np.array(tuple(map(int, elems[2::3])))
+                images[image_id] = Image(
+                    id=image_id, qvec=qvec, tvec=tvec,
+                    camera_id=camera_id, name=image_name,
+                    xys=xys, point3D_ids=point3D_ids)
+    return images
+
+
+def read_images_binary(path_to_model_file):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::ReadImagesBinary(const std::string& path)
+        void Reconstruction::WriteImagesBinary(const std::string& path)
+    """
+    images = {}
+    with open(path_to_model_file, "rb") as fid:
+        num_reg_images = read_next_bytes(fid, 8, "Q")[0]
+        for image_index in range(num_reg_images):
+            binary_image_properties = read_next_bytes(
+                fid, num_bytes=64, format_char_sequence="idddddddi")
+            image_id = binary_image_properties[0]
+            qvec = np.array(binary_image_properties[1:5])
+            tvec = np.array(binary_image_properties[5:8])
+            camera_id = binary_image_properties[8]
+            image_name = ""
+            current_char = read_next_bytes(fid, 1, "c")[0]
+            while current_char != b"\x00":   # look for the ASCII 0 entry
+                image_name += current_char.decode("utf-8")
+                current_char = read_next_bytes(fid, 1, "c")[0]
+            num_points2D = read_next_bytes(fid, num_bytes=8,
+                                           format_char_sequence="Q")[0]
+            x_y_id_s = read_next_bytes(fid, num_bytes=24*num_points2D,
+                                       format_char_sequence="ddq"*num_points2D)
+            xys = np.column_stack([tuple(map(float, x_y_id_s[0::3])),
+                                   tuple(map(float, x_y_id_s[1::3]))])
+            point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3])))
+            images[image_id] = Image(
+                id=image_id, qvec=qvec, tvec=tvec,
+                camera_id=camera_id, name=image_name,
+                xys=xys, point3D_ids=point3D_ids)
+    return images
+
+
+def read_points3D_text(path):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::ReadPoints3DText(const std::string& path)
+        void Reconstruction::WritePoints3DText(const std::string& path)
+    """
+    points3D = {}
+    with open(path, "r") as fid:
+        while True:
+            line = fid.readline()
+            if not line:
+                break
+            line = line.strip()
+            if len(line) > 0 and line[0] != "#":
+                elems = line.split()
+                point3D_id = int(elems[0])
+                xyz = np.array(tuple(map(float, elems[1:4])))
+                rgb = np.array(tuple(map(int, elems[4:7])))
+                error = float(elems[7])
+                image_ids = np.array(tuple(map(int, elems[8::2])))
+                point2D_idxs = np.array(tuple(map(int, elems[9::2])))
+                points3D[point3D_id] = Point3D(id=point3D_id, xyz=xyz, rgb=rgb,
+                                               error=error, image_ids=image_ids,
+                                               point2D_idxs=point2D_idxs)
+    return points3D
+
+
+def read_points3d_binary(path_to_model_file):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::ReadPoints3DBinary(const std::string& path)
+        void Reconstruction::WritePoints3DBinary(const std::string& path)
+    """
+    points3D = {}
+    with open(path_to_model_file, "rb") as fid:
+        num_points = read_next_bytes(fid, 8, "Q")[0]
+        for point_line_index in range(num_points):
+            binary_point_line_properties = read_next_bytes(
+                fid, num_bytes=43, format_char_sequence="QdddBBBd")
+            point3D_id = binary_point_line_properties[0]
+            xyz = np.array(binary_point_line_properties[1:4])
+            rgb = np.array(binary_point_line_properties[4:7])
+            error = np.array(binary_point_line_properties[7])
+            track_length = read_next_bytes(
+                fid, num_bytes=8, format_char_sequence="Q")[0]
+            track_elems = read_next_bytes(
+                fid, num_bytes=8*track_length,
+                format_char_sequence="ii"*track_length)
+            image_ids = np.array(tuple(map(int, track_elems[0::2])))
+            point2D_idxs = np.array(tuple(map(int, track_elems[1::2])))
+            points3D[point3D_id] = Point3D(
+                id=point3D_id, xyz=xyz, rgb=rgb,
+                error=error, image_ids=image_ids,
+                point2D_idxs=point2D_idxs)
+    return points3D
+
+
+def read_model(path, ext):
+    if ext == ".txt":
+        cameras = read_cameras_text(os.path.join(path, "cameras" + ext))
+        images = read_images_text(os.path.join(path, "images" + ext))
+        points3D = read_points3D_text(os.path.join(path, "points3D") + ext)
+    else:
+        cameras = read_cameras_binary(os.path.join(path, "cameras" + ext))
+        images = read_images_binary(os.path.join(path, "images" + ext))
+        points3D = read_points3d_binary(os.path.join(path, "points3D") + ext)
+    return cameras, images, points3D
+
+
+def qvec2rotmat(qvec):
+    return np.array([
+        [1 - 2 * qvec[2]**2 - 2 * qvec[3]**2,
+         2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
+         2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]],
+        [2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
+         1 - 2 * qvec[1]**2 - 2 * qvec[3]**2,
+         2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]],
+        [2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
+         2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
+         1 - 2 * qvec[1]**2 - 2 * qvec[2]**2]])
+
+
+def rotmat2qvec(R):
+    Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat
+    K = np.array([
+        [Rxx - Ryy - Rzz, 0, 0, 0],
+        [Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0],
+        [Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0],
+        [Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz]]) / 3.0
+    eigvals, eigvecs = np.linalg.eigh(K)
+    qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)]
+    if qvec[0] < 0:
+        qvec *= -1
+    return qvec
+
+
+def main():
+    if len(sys.argv) != 3:
+        print("Usage: python read_model.py path/to/model/folder [.txt,.bin]")
+        return
+
+    cameras, images, points3D = read_model(path=sys.argv[1], ext=sys.argv[2])
+
+    print("num_cameras:", len(cameras))
+    print("num_images:", len(images))
+    print("num_points3D:", len(points3D))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/poses/colmap_wrapper.py b/poses/colmap_wrapper.py
new file mode 100644
index 0000000..53ab08f
--- /dev/null
+++ b/poses/colmap_wrapper.py
@@ -0,0 +1,71 @@
+import os
+import subprocess
+import logging
+
+# $ DATASET_PATH=/path/to/dataset
+
+# $ colmap feature_extractor \
+#    --database_path $DATASET_PATH/database.db \
+#    --image_path $DATASET_PATH/images
+
+# $ colmap exhaustive_matcher \
+#    --database_path $DATASET_PATH/database.db
+
+# $ mkdir $DATASET_PATH/sparse
+
+# $ colmap mapper \
+#     --database_path $DATASET_PATH/database.db \
+#     --image_path $DATASET_PATH/images \
+#     --output_path $DATASET_PATH/sparse
+
+# $ mkdir $DATASET_PATH/dense
+
+
+def run_colmap(basedir, match_type):
+    """Simple wrapper for COLMAP"""
+
+    logfile_name = os.path.join(basedir, 'colmap_output.txt')
+    logfile = open(logfile_name, 'w')
+
+    feature_extractor_args = [
+        'colmap', 'feature_extractor',
+            '--database_path', os.path.join(basedir, 'database.db'),
+            '--image_path', os.path.join(basedir, 'images'),
+            '--ImageReader.single_camera', '1',
+            # '--SiftExtraction.use_gpu', '0',
+    ]
+    logging.debug("Extracting features.")
+    feat_output = subprocess.check_output(feature_extractor_args, universal_newlines=True)
+    logfile.write(feat_output)
+
+    exhaustive_matcher_args = [
+        'colmap', match_type,
+            '--database_path', os.path.join(basedir, 'database.db'),
+    ]
+
+    logging.debug("Matching features.")
+    match_output = subprocess.check_output(exhaustive_matcher_args, universal_newlines=True)
+    logfile.write(match_output)
+
+    p = os.path.join(basedir, 'sparse')
+    if not os.path.exists(p):
+        os.makedirs(p)
+
+    mapper_args = [
+        'colmap', 'mapper',
+            '--database_path', os.path.join(basedir, 'database.db'),
+            '--image_path', os.path.join(basedir, 'images'),
+            '--output_path', os.path.join(basedir, 'sparse'), # --export_path changed to --output_path in colmap 3.6
+            '--Mapper.num_threads', '16',
+            '--Mapper.init_min_tri_angle', '4',
+            '--Mapper.multiple_models', '0',
+            '--Mapper.extract_colors', '0',
+    ]
+
+    logging.debug("Creating sparse map")
+    map_output = subprocess.check_output(mapper_args, universal_newlines=True)
+    logfile.write(map_output)
+    logfile.close()
+
+    logging.info("Finished running COLMAP. Logfile written in %s",
+            logfile_name)
diff --git a/poses/pose_utils.py b/poses/pose_utils.py
new file mode 100644
index 0000000..03cb142
--- /dev/null
+++ b/poses/pose_utils.py
@@ -0,0 +1,138 @@
+import os
+import logging
+import typing
+
+import numpy as np
+
+from colmap_wrapper import run_colmap
+import colmap_read_model as read_model
+#from nerf_homemade.poses.colmap_wrapper import run_colmap
+#import nerf_homemade.poses.colmap_read_model as read_model
+
+
+def gen_poses(basedir: str, match_type: str='exhaustive') -> None:
+    """
+    Geneate or retreive camera poses.
+
+    Retrieve the cameras either by generating using COLMAP or reading them if
+    they already have been written.
+
+    Parameters
+    ----------
+    basedir: str
+        The path of the directory either containing raw images or the
+        arborescence of a COLMAP directory if camera poses have already been
+        computed
+    match_type: {'exhaustive', 'sequential'}
+        The type of match COLMAP should perform if it needs to be run.
+        Defaults to 'exhaustive'.
+    """
+
+    files_needed = ["cameras.bin", "images.bin", "points3D.bin"]
+    
+    path_to_sparse = os.path.join(basedir, "sparse/0")
+    if os.path.exists(path_to_sparse):
+        existing_files = os.listdir(path_to_sparse)
+    else:
+        existing_files = []
+    
+    if not all([f in existing_files for f in files_needed]):
+        logging.info("Running COLMAP")
+        run_colmap(basedir, match_type)
+    else:
+        logging.info("Files genreated by COLMAP found. Skipping running COLMAP.")
+
+    logging.debug("Loading COLMAP data")
+    poses, points_3d, perm = load_colmap_data(basedir)
+
+    logging.debug("Saving COLMAP data to npy")
+    save_poses(basedir, poses, points_3d, perm)
+
+def load_colmap_data(basedir: str) -> (np.ndarray, dict, np.ndarray):
+    """
+    Load data from a COLMAP arborescence.
+
+    Parameters
+    ----------
+    basedir: str
+        The path of the directory which contains the COLMAP arborescence
+
+    Returns
+    -------
+    poses: numpy.ndarray
+        List of poses for each image.
+    pts3d:
+        List of 3D points for each image.
+    perm:
+        Index list ordered by name of images.
+        Should be [1, 2, ..., N] if well ordered.
+    """
+    # append prefix
+    basedir = os.path.join(basedir, "sparse/0/")
+
+    # read cameras data
+    cameras_file = os.path.join(basedir, "cameras.bin")
+    cameras_data = read_model.read_cameras_binary(cameras_file)
+    logging.debug(f"Loading camera model from {cameras_file}")
+
+    # extract intrinsic values for the camera
+    # asumption is made that it is unique
+    camera_h = cameras_data[1].height
+    camera_w = cameras_data[1].width
+    camera_f = cameras_data[1].params[0]
+    hwf = np.array([camera_h, camera_w, camera_f]).reshape([3,1])
+    logging.debug(f"Number of cameras: {len(cameras_data)}")
+            
+    # read images data
+    images_file = os.path.join(basedir, "images.bin")
+    images_data = read_model.read_images_binary(images_file)
+
+    w2c_mats = []
+    bottom = np.array([0., 0., 0., 1.]).reshape([1, 4])
+
+    # sort by name
+    names = [images_data[k].name for k in images_data]
+    perm = np.argsort(names)  
+    # equivalent to a range since dict is index by number from 1 to N 
+
+    # create camera matrix
+    for k in images_data:
+        im = images_data[k]
+        R = im.qvec2rotmat()
+        t = im.tvec.reshape([3,1])
+        m = np.concatenate([np.concatenate([R, t], 1), bottom], 0)
+        w2c_mats.append(m)
+
+    w2c_mats = np.stack(w2c_mats, 0)
+    c2w_mats = np.linalg.inv(w2c_mats)
+
+    poses = c2w_mats[:, :3, :4].transpose([1,2,0])
+    poses = np.concatenate([poses, np.tile(hwf[..., np.newaxis], [1,1,poses.shape[-1]])], 1)
+
+    # read 3d points data
+    pts3d_file = os.path.join(basedir, "points3D.bin")
+    pts3d = read_model.read_points3d_binary(pts3d_file)
+
+    # must switch to [-u, r, -t] from [r, -u, t], NOT [r, u, -t]
+    poses = np.concatenate([poses[:, 1:2, :], poses[:, 0:1, :], -poses[:, 2:3, :], poses[:, 3:4, :], poses[:, 4:5, :]], 1)
+
+    return poses, pts3d, perm
+
+def save_poses(basedir, poses, pts3d, perm) -> None:
+    """
+    Save the COLMAP data in a `.npy` format.
+
+    Parameters
+    ----------
+    basedir: str
+        The path of the directory in which to save the data.
+        Data will be saved in `basedir/poses_bounds.npy`.
+    poses:
+        The list of poses
+    pts3d:
+        The list of 3d points
+    perm:
+        The sorted index of the array
+    """
+
+    # TODO