diff --git a/colmap_runner/database.py b/colmap_runner/database.py
new file mode 100644
index 0000000..42a9730
--- /dev/null
+++ b/colmap_runner/database.py
@@ -0,0 +1,352 @@
+# 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-demuc-dot-de)
+
+# This script is based on an original implementation by True Price.
+
+import sys
+import sqlite3
+import numpy as np
+
+
+IS_PYTHON3 = sys.version_info[0] >= 3
+
+MAX_IMAGE_ID = 2**31 - 1
+
+CREATE_CAMERAS_TABLE = """CREATE TABLE IF NOT EXISTS cameras (
+    camera_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
+    model INTEGER NOT NULL,
+    width INTEGER NOT NULL,
+    height INTEGER NOT NULL,
+    params BLOB,
+    prior_focal_length INTEGER NOT NULL)"""
+
+CREATE_DESCRIPTORS_TABLE = """CREATE TABLE IF NOT EXISTS descriptors (
+    image_id INTEGER PRIMARY KEY NOT NULL,
+    rows INTEGER NOT NULL,
+    cols INTEGER NOT NULL,
+    data_500 BLOB,
+    FOREIGN KEY(image_id) REFERENCES images(image_id) ON DELETE CASCADE)"""
+
+CREATE_IMAGES_TABLE = """CREATE TABLE IF NOT EXISTS images (
+    image_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
+    name TEXT NOT NULL UNIQUE,
+    camera_id INTEGER NOT NULL,
+    prior_qw REAL,
+    prior_qx REAL,
+    prior_qy REAL,
+    prior_qz REAL,
+    prior_tx REAL,
+    prior_ty REAL,
+    prior_tz REAL,
+    CONSTRAINT image_id_check CHECK(image_id >= 0 and image_id < {}),
+    FOREIGN KEY(camera_id) REFERENCES cameras(camera_id))
+""".format(MAX_IMAGE_ID)
+
+CREATE_TWO_VIEW_GEOMETRIES_TABLE = """
+CREATE TABLE IF NOT EXISTS two_view_geometries (
+    pair_id INTEGER PRIMARY KEY NOT NULL,
+    rows INTEGER NOT NULL,
+    cols INTEGER NOT NULL,
+    data_500 BLOB,
+    config INTEGER NOT NULL,
+    F BLOB,
+    E BLOB,
+    H BLOB)
+"""
+
+CREATE_KEYPOINTS_TABLE = """CREATE TABLE IF NOT EXISTS keypoints (
+    image_id INTEGER PRIMARY KEY NOT NULL,
+    rows INTEGER NOT NULL,
+    cols INTEGER NOT NULL,
+    data_500 BLOB,
+    FOREIGN KEY(image_id) REFERENCES images(image_id) ON DELETE CASCADE)
+"""
+
+CREATE_MATCHES_TABLE = """CREATE TABLE IF NOT EXISTS matches (
+    pair_id INTEGER PRIMARY KEY NOT NULL,
+    rows INTEGER NOT NULL,
+    cols INTEGER NOT NULL,
+    data_500 BLOB)"""
+
+CREATE_NAME_INDEX = \
+    "CREATE UNIQUE INDEX IF NOT EXISTS index_name ON images(name)"
+
+CREATE_ALL = "; ".join([
+    CREATE_CAMERAS_TABLE,
+    CREATE_IMAGES_TABLE,
+    CREATE_KEYPOINTS_TABLE,
+    CREATE_DESCRIPTORS_TABLE,
+    CREATE_MATCHES_TABLE,
+    CREATE_TWO_VIEW_GEOMETRIES_TABLE,
+    CREATE_NAME_INDEX
+])
+
+
+def image_ids_to_pair_id(image_id1, image_id2):
+    if image_id1 > image_id2:
+        image_id1, image_id2 = image_id2, image_id1
+    return image_id1 * MAX_IMAGE_ID + image_id2
+
+
+def pair_id_to_image_ids(pair_id):
+    image_id2 = pair_id % MAX_IMAGE_ID
+    image_id1 = (pair_id - image_id2) / MAX_IMAGE_ID
+    return image_id1, image_id2
+
+
+def array_to_blob(array):
+    if IS_PYTHON3:
+        return array.tostring()
+    else:
+        return np.getbuffer(array)
+
+
+def blob_to_array(blob, dtype, shape=(-1,)):
+    if IS_PYTHON3:
+        return np.fromstring(blob, dtype=dtype).reshape(*shape)
+    else:
+        return np.frombuffer(blob, dtype=dtype).reshape(*shape)
+
+
+class COLMAPDatabase(sqlite3.Connection):
+
+    @staticmethod
+    def connect(database_path):
+        return sqlite3.connect(database_path, factory=COLMAPDatabase)
+
+
+    def __init__(self, *args, **kwargs):
+        super(COLMAPDatabase, self).__init__(*args, **kwargs)
+
+        self.create_tables = lambda: self.executescript(CREATE_ALL)
+        self.create_cameras_table = \
+            lambda: self.executescript(CREATE_CAMERAS_TABLE)
+        self.create_descriptors_table = \
+            lambda: self.executescript(CREATE_DESCRIPTORS_TABLE)
+        self.create_images_table = \
+            lambda: self.executescript(CREATE_IMAGES_TABLE)
+        self.create_two_view_geometries_table = \
+            lambda: self.executescript(CREATE_TWO_VIEW_GEOMETRIES_TABLE)
+        self.create_keypoints_table = \
+            lambda: self.executescript(CREATE_KEYPOINTS_TABLE)
+        self.create_matches_table = \
+            lambda: self.executescript(CREATE_MATCHES_TABLE)
+        self.create_name_index = lambda: self.executescript(CREATE_NAME_INDEX)
+
+    def add_camera(self, model, width, height, params,
+                   prior_focal_length=False, camera_id=None):
+        params = np.asarray(params, np.float64)
+        cursor = self.execute(
+            "INSERT INTO cameras VALUES (?, ?, ?, ?, ?, ?)",
+            (camera_id, model, width, height, array_to_blob(params),
+             prior_focal_length))
+        return cursor.lastrowid
+
+    def add_image(self, name, camera_id,
+                  prior_q=np.zeros(4), prior_t=np.zeros(3), image_id=None):
+        cursor = self.execute(
+            "INSERT INTO images VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
+            (image_id, name, camera_id, prior_q[0], prior_q[1], prior_q[2],
+             prior_q[3], prior_t[0], prior_t[1], prior_t[2]))
+        return cursor.lastrowid
+
+    def add_keypoints(self, image_id, keypoints):
+        assert(len(keypoints.shape) == 2)
+        assert(keypoints.shape[1] in [2, 4, 6])
+
+        keypoints = np.asarray(keypoints, np.float32)
+        self.execute(
+            "INSERT INTO keypoints VALUES (?, ?, ?, ?)",
+            (image_id,) + keypoints.shape + (array_to_blob(keypoints),))
+
+    def add_descriptors(self, image_id, descriptors):
+        descriptors = np.ascontiguousarray(descriptors, np.uint8)
+        self.execute(
+            "INSERT INTO descriptors VALUES (?, ?, ?, ?)",
+            (image_id,) + descriptors.shape + (array_to_blob(descriptors),))
+
+    def add_matches(self, image_id1, image_id2, matches):
+        assert(len(matches.shape) == 2)
+        assert(matches.shape[1] == 2)
+
+        if image_id1 > image_id2:
+            matches = matches[:,::-1]
+
+        pair_id = image_ids_to_pair_id(image_id1, image_id2)
+        matches = np.asarray(matches, np.uint32)
+        self.execute(
+            "INSERT INTO matches VALUES (?, ?, ?, ?)",
+            (pair_id,) + matches.shape + (array_to_blob(matches),))
+
+    def add_two_view_geometry(self, image_id1, image_id2, matches,
+                              F=np.eye(3), E=np.eye(3), H=np.eye(3), config=2):
+        assert(len(matches.shape) == 2)
+        assert(matches.shape[1] == 2)
+
+        if image_id1 > image_id2:
+            matches = matches[:,::-1]
+
+        pair_id = image_ids_to_pair_id(image_id1, image_id2)
+        matches = np.asarray(matches, np.uint32)
+        F = np.asarray(F, dtype=np.float64)
+        E = np.asarray(E, dtype=np.float64)
+        H = np.asarray(H, dtype=np.float64)
+        self.execute(
+            "INSERT INTO two_view_geometries VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
+            (pair_id,) + matches.shape + (array_to_blob(matches), config,
+             array_to_blob(F), array_to_blob(E), array_to_blob(H)))
+
+
+def example_usage():
+    import os
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--database_path", default="database.db")
+    args = parser.parse_args()
+
+    if os.path.exists(args.database_path):
+        logging.info("ERROR: database path already exists -- will not modify it.")
+        return
+
+    # Open the database.
+
+    db = COLMAPDatabase.connect(args.database_path)
+
+    # For convenience, try creating all the tables upfront.
+
+    db.create_tables()
+
+    # Create dummy cameras.
+
+    model1, width1, height1, params1 = \
+        0, 1024, 768, np.array((1024., 512., 384.))
+    model2, width2, height2, params2 = \
+        2, 1024, 768, np.array((1024., 512., 384., 0.1))
+
+    camera_id1 = db.add_camera(model1, width1, height1, params1)
+    camera_id2 = db.add_camera(model2, width2, height2, params2)
+
+    # Create dummy images.
+
+    image_id1 = db.add_image("image1.png", camera_id1)
+    image_id2 = db.add_image("image2.png", camera_id1)
+    image_id3 = db.add_image("image3.png", camera_id2)
+    image_id4 = db.add_image("image4.png", camera_id2)
+
+    # Create dummy keypoints.
+    #
+    # Note that COLMAP supports:
+    #      - 2D keypoints: (x, y)
+    #      - 4D keypoints: (x, y, theta, scale)
+    #      - 6D affine keypoints: (x, y, a_11, a_12, a_21, a_22)
+
+    num_keypoints = 1000
+    keypoints1 = np.random.rand(num_keypoints, 2) * (width1, height1)
+    keypoints2 = np.random.rand(num_keypoints, 2) * (width1, height1)
+    keypoints3 = np.random.rand(num_keypoints, 2) * (width2, height2)
+    keypoints4 = np.random.rand(num_keypoints, 2) * (width2, height2)
+
+    db.add_keypoints(image_id1, keypoints1)
+    db.add_keypoints(image_id2, keypoints2)
+    db.add_keypoints(image_id3, keypoints3)
+    db.add_keypoints(image_id4, keypoints4)
+
+    # Create dummy matches.
+
+    M = 50
+    matches12 = np.random.randint(num_keypoints, size=(M, 2))
+    matches23 = np.random.randint(num_keypoints, size=(M, 2))
+    matches34 = np.random.randint(num_keypoints, size=(M, 2))
+
+    db.add_matches(image_id1, image_id2, matches12)
+    db.add_matches(image_id2, image_id3, matches23)
+    db.add_matches(image_id3, image_id4, matches34)
+
+    # Commit the data_500 to the file.
+
+    db.commit()
+
+    # Read and check cameras.
+
+    rows = db.execute("SELECT * FROM cameras")
+
+    camera_id, model, width, height, params, prior = next(rows)
+    params = blob_to_array(params, np.float64)
+    assert camera_id == camera_id1
+    assert model == model1 and width == width1 and height == height1
+    assert np.allclose(params, params1)
+
+    camera_id, model, width, height, params, prior = next(rows)
+    params = blob_to_array(params, np.float64)
+    assert camera_id == camera_id2
+    assert model == model2 and width == width2 and height == height2
+    assert np.allclose(params, params2)
+
+    # Read and check keypoints.
+
+    keypoints = dict(
+        (image_id, blob_to_array(data, np.float32, (-1, 2)))
+        for image_id, data in db.execute(
+            "SELECT image_id, data_500 FROM keypoints"))
+
+    assert np.allclose(keypoints[image_id1], keypoints1)
+    assert np.allclose(keypoints[image_id2], keypoints2)
+    assert np.allclose(keypoints[image_id3], keypoints3)
+    assert np.allclose(keypoints[image_id4], keypoints4)
+
+    # Read and check matches.
+
+    pair_ids = [image_ids_to_pair_id(*pair) for pair in
+                ((image_id1, image_id2),
+                 (image_id2, image_id3),
+                 (image_id3, image_id4))]
+
+    matches = dict(
+        (pair_id_to_image_ids(pair_id),
+         blob_to_array(data, np.uint32, (-1, 2)))
+        for pair_id, data in db.execute("SELECT pair_id, data_500 FROM matches")
+    )
+
+    assert np.all(matches[(image_id1, image_id2)] == matches12)
+    assert np.all(matches[(image_id2, image_id3)] == matches23)
+    assert np.all(matches[(image_id3, image_id4)] == matches34)
+
+    # Clean up.
+
+    db.close()
+
+    if os.path.exists(args.database_path):
+        os.remove(args.database_path)
+
+
+if __name__ == "__main__":
+    example_usage()
diff --git a/colmap_runner/run_colmap_posed.py b/colmap_runner/run_colmap_posed.py
new file mode 100644
index 0000000..5d455d7
--- /dev/null
+++ b/colmap_runner/run_colmap_posed.py
@@ -0,0 +1,295 @@
+import os
+import json
+from database import COLMAPDatabase
+from pyquaternion import Quaternion
+import numpy as np
+import imageio
+import subprocess
+
+
+def bash_run(cmd):
+    # local install of colmap
+    env = os.environ.copy()
+    env['LD_LIBRARY_PATH'] = '/home/zhangka2/code/colmap/build/__install__/lib'
+    
+    colmap_bin = '/home/zhangka2/code/colmap/build/__install__/bin/colmap'
+    cmd = colmap_bin + ' ' + cmd
+    print('\nRunning cmd: ', cmd)
+
+    subprocess.check_call(['/bin/bash', '-c', cmd], env=env)
+
+
+gpu_index = '-1'
+
+
+def run_sift_matching(img_dir, db_file):
+    print('Running sift matching...')
+
+    # if os.path.exists(db_file): # otherwise colmap will skip sift matching
+    #     os.remove(db_file)
+
+    # feature extraction
+    # if there's no attached display, cannot use feature extractor with GPU
+    cmd = ' feature_extractor --database_path {} \
+                                    --image_path {} \
+                                    --ImageReader.camera_model PINHOLE \
+                                    --SiftExtraction.max_image_size 5000  \
+                                    --SiftExtraction.estimate_affine_shape 0 \
+                                    --SiftExtraction.domain_size_pooling 1 \
+                                    --SiftExtraction.num_threads 32 \
+                                    --SiftExtraction.use_gpu 0 \
+                                    --SiftExtraction.gpu_index {}'.format(db_file, img_dir, gpu_index)
+    bash_run(cmd)
+
+    # feature matching
+    cmd = ' exhaustive_matcher --database_path {} \
+                                     --SiftMatching.guided_matching 1 \
+                                     --SiftMatching.use_gpu 0 \
+                                     --SiftMatching.gpu_index {}'.format(db_file, gpu_index)
+
+    bash_run(cmd)
+
+
+def create_init_files(pinhole_dict_file, db_file, out_dir):
+    if not os.path.exists(out_dir):
+        os.mkdir(out_dir)
+
+    # create template
+    with open(pinhole_dict_file) as fp:
+        pinhole_dict = json.load(fp)
+
+    template = {}
+    cameras_line_template = '{camera_id} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\n'
+    images_line_template = '{image_id} {qw} {qx} {qy} {qz} {tx} {ty} {tz} {camera_id} {image_name}\n\n'
+
+    for img_name in pinhole_dict:
+        # w, h, fx, fy, cx, cy, qvec, t
+        params = pinhole_dict[img_name]
+        w = params[0]
+        h = params[1]
+        fx = params[2]
+        fy = params[3]
+        cx = params[4]
+        cy = params[5]
+        qvec = params[6:10]
+        tvec = params[10:13]
+
+        cam_line = cameras_line_template.format(camera_id="{camera_id}", width=w, height=h, fx=fx, fy=fy, cx=cx, cy=cy)
+        img_line = images_line_template.format(image_id="{image_id}", qw=qvec[0], qx=qvec[1], qy=qvec[2], qz=qvec[3],
+                                            tx=tvec[0], ty=tvec[1], tz=tvec[2], camera_id="{camera_id}", image_name=img_name)
+        template[img_name] = (cam_line, img_line)
+
+    # read database
+    db = COLMAPDatabase.connect(db_file)
+    table_images = db.execute("SELECT * FROM images")
+    img_name2id_dict = {}
+    for row in table_images:
+        img_name2id_dict[row[1]] = row[0]
+
+    cameras_txt_lines = []
+    images_txt_lines = []
+    for img_name, img_id in img_name2id_dict.items():
+        camera_line = template[img_name][0].format(camera_id=img_id)
+        cameras_txt_lines.append(camera_line)
+
+        image_line = template[img_name][1].format(image_id=img_id, camera_id=img_id)
+        images_txt_lines.append(image_line)
+
+    with open(os.path.join(out_dir, 'cameras.txt'), 'w') as fp:
+        fp.writelines(cameras_txt_lines)
+
+    with open(os.path.join(out_dir, 'images.txt'), 'w') as fp:
+        fp.writelines(images_txt_lines)
+        fp.write('\n')
+
+    # create an empty points3D.txt
+    fp = open(os.path.join(out_dir, 'points3D.txt'), 'w')
+    fp.close()
+
+
+def run_point_triangulation(img_dir, db_file, out_dir):
+    print('Running point triangulation...')
+
+    # triangulate points
+    cmd = ' point_triangulator  --database_path {} \
+                                      --image_path {} \
+                                      --input_path {} \
+                                      --output_path {} \
+                                      --Mapper.tri_ignore_two_view_tracks 1'.format(db_file, img_dir, out_dir, out_dir)
+    bash_run(cmd)
+
+
+# this step is optional
+def run_global_ba(in_dir, out_dir):
+    print('Running global BA...')
+    if not os.path.exists(out_dir):
+        os.mkdir(out_dir)
+
+    cmd = ' bundle_adjuster --input_path {in_dir} --output_path {out_dir}'.format(in_dir=in_dir, out_dir=out_dir)
+
+    bash_run(cmd)
+
+
+def prepare_mvs(img_dir, sfm_dir, mvs_dir):
+    if not os.path.exists(mvs_dir):
+        os.mkdir(mvs_dir)
+
+    images_symlink = os.path.join(mvs_dir, 'images')
+    if os.path.exists(images_symlink):
+       os.unlink(images_symlink)
+    os.symlink(os.path.relpath(img_dir, mvs_dir),
+               images_symlink)
+
+    sparse_symlink = os.path.join(mvs_dir, 'sparse')
+    if os.path.exists(sparse_symlink):
+       os.unlink(sparse_symlink)
+    os.symlink(os.path.relpath(sfm_dir, mvs_dir),
+               sparse_symlink)
+
+    # prepare stereo directory
+    stereo_dir = os.path.join(mvs_dir, 'stereo')
+    for subdir in [stereo_dir, 
+                   os.path.join(stereo_dir, 'depth_maps'),
+                   os.path.join(stereo_dir, 'normal_maps'),
+                   os.path.join(stereo_dir, 'consistency_graphs')]:
+        if not os.path.exists(subdir):
+            os.mkdir(subdir)
+
+    # write patch-match.cfg and fusion.cfg
+    image_names = sorted(os.listdir(os.path.join(mvs_dir, 'images')))
+
+    with open(os.path.join(stereo_dir, 'patch-match.cfg'), 'w') as fp:
+        for img_name in image_names:
+            fp.write(img_name + '\n__auto__, 20\n')
+            
+            # use all images
+            # fp.write(img_name + '\n__all__\n')
+
+            # randomly choose 20 images
+            # from random import shuffle
+            # candi_src_images = [x for x in image_names if x != img_name]
+            # shuffle(candi_src_images)
+            # max_src_images = 10
+            # fp.write(img_name + '\n' + ', '.join(candi_src_images[:max_src_images]) + '\n')
+
+    with open(os.path.join(stereo_dir, 'fusion.cfg'), 'w') as fp:
+        for img_name in image_names:
+            fp.write(img_name + '\n')
+
+
+def run_photometric_mvs(mvs_dir, window_radius):
+    print('Running photometric MVS...')
+
+    cmd = ' patch_match_stereo --workspace_path {} \
+                    --PatchMatchStereo.window_radius {} \
+                    --PatchMatchStereo.min_triangulation_angle 3.0 \
+                    --PatchMatchStereo.filter 1 \
+                    --PatchMatchStereo.geom_consistency 1 \
+                    --PatchMatchStereo.gpu_index={} \
+                    --PatchMatchStereo.num_samples 15 \
+                    --PatchMatchStereo.num_iterations 12'.format(mvs_dir,
+                                                                 window_radius, gpu_index)
+
+    bash_run(cmd)
+
+
+def run_fuse(mvs_dir, out_ply):
+    print('Running depth fusion...')
+
+    cmd = ' stereo_fusion --workspace_path {} \
+                         --output_path {} \
+                         --input_type geometric'.format(mvs_dir, out_ply)
+    bash_run(cmd)
+
+
+def run_possion_mesher(in_ply, out_ply, trim):
+    print('Running possion mesher...')
+
+    cmd = ' poisson_mesher \
+            --input_path {} \
+            --output_path {} \
+            --PoissonMeshing.trim {}'.format(in_ply, out_ply, trim)
+
+    bash_run(cmd)
+
+
+def main(img_dir, pinhole_dict_file, out_dir):
+    if not os.path.exists(out_dir):
+        os.mkdir(out_dir)
+
+    db_file = os.path.join(out_dir, 'database.db')
+    run_sift_matching(img_dir, db_file)
+
+    sfm_dir = os.path.join(out_dir, 'sfm')
+    create_init_files(pinhole_dict_file, db_file, sfm_dir)
+    run_point_triangulation(img_dir, db_file, sfm_dir)
+
+    # # optional
+    # run_global_ba(sfm_dir, sfm_dir)
+
+    mvs_dir = os.path.join(out_dir, 'mvs')
+    prepare_mvs(img_dir, sfm_dir, mvs_dir)
+    run_photometric_mvs(mvs_dir, window_radius=5)
+
+    out_ply = os.path.join(out_dir, 'fused.ply')
+    run_fuse(mvs_dir, out_ply)
+
+    out_mesh_ply = os.path.join(out_dir, 'meshed_trim_3.ply')
+    run_possion_mesher(out_ply, out_mesh_ply, trim=3)
+
+
+def convert_cam_dict_to_pinhole_dict(cam_dict_file, pinhole_dict_file, img_dir):
+    print('Writing pinhole_dict to: ', pinhole_dict_file)
+
+    with open(cam_dict_file) as fp:
+        cam_dict = json.load(fp)
+
+    pinhole_dict = {}
+    for img_name in cam_dict:
+        data_item = cam_dict[img_name]
+        if 'img_size' in data_item:
+            w, h = data_item['img_size']
+        else:
+            im = imageio.imread(os.path.join(img_dir, img_name))
+            h, w = im.shape[:2]
+
+        K = np.array(data_item['K']).reshape((4, 4))
+        W2C = np.array(data_item['W2C']).reshape((4, 4))
+
+        # params
+        fx = K[0, 0]
+        fy = K[1, 1]
+        assert(np.isclose(K[0, 1], 0.))
+        cx = K[0, 2]
+        cy = K[1, 2]
+
+        print(img_name)
+        R = W2C[:3, :3]
+        print(R)
+        u, s_old, vh = np.linalg.svd(R, full_matrices=False)
+        s = np.round(s_old)
+        print('s: {} ---> {}'.format(s_old, s))
+        R = np.dot(u * s, vh)
+
+        qvec = Quaternion(matrix=R)
+        tvec = W2C[:3, 3]
+
+        params = [w, h, fx, fy, cx, cy, 
+                  qvec[0], qvec[1], qvec[2], qvec[3],
+                  tvec[0], tvec[1], tvec[2]]
+        pinhole_dict[img_name] = params
+
+    with open(pinhole_dict_file, 'w') as fp:
+        json.dump(pinhole_dict, fp, indent=2, sort_keys=True)
+
+
+if __name__ == '__main__':
+    img_dir = ''
+    cam_dict_file = ''
+    out_dir = ''
+
+    os.makedirs(out_dir, exist_ok=True)
+    pinhole_dict_file = os.path.join(out_dir, 'pinhole_dict.json')
+    convert_cam_dict_to_pinhole_dict(cam_dict_file, pinhole_dict_file, img_dir)
+
+    main(img_dir, pinhole_dict_file, out_dir)