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COLMAP利用已知相机内外参重建NeRF的blender模型

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文章目录

前言

本文的目的是根据已知相机参数的blender模型，使用colmap进行稀疏重建和稠密重建。使用的blender数据是NeRF提供的synthetic数据集中的lego模型，其中的几张图片如下：

COLMAP利用已知相机内外参重建NeRF的blender模型

; 一、数据准备

文件夹应按如下层级组织：

 E:\rootpath
 ├─created
 │  └─sparse
 │   +── cameras.txt
 │   +── images.txt
 │   +── points3D.txt
 ├─dense
 ├─images
 │   +── r_0.png
 │   +── r_1.png
 │   +── ...

 ├─model
 └─triangulated
     └─sparse
 +── transforms_train.json
 +── blender_camera2colmap.py
 +── transform_colmap_camera.py

其中 created/sparse 文件夹下的 cameras.txt 对应我们指定的相机内参， images.txt 对应每张图片的相机外参信息， points3D.txt 对应稠密重建需要用到的稀疏点云。 dense 文件夹下保存colmap稠密重建结果， images 文件夹下存放输入的图片，也就是NeRF的训练视图， model 文件夹下存放colmap导出的稀疏重建结果， triangulated/sparse 文件夹下保存colmap稀疏重建结果， transforms_train.json 是NeRF blender数据集提供的真实的相机内外参数据，最后两个python文件是后面要用到的脚本。

二、从blender数据构造colmap数据集

这一步是为了读取NeRF的blender相机参数数据，转换成colmap可以使用的数据格式。blender相机参数采用右手坐标系，相机的位姿用于从相机坐标系向世界坐标系转换，以旋转矩阵 R 和平移向量 T 的格式给出；colmap相机参数采用opecv格式的坐标系，相机的位姿用于从世界坐标系向相机坐标系转换，以四元数 Quat 和平移向量 T 的格式给出，因此需要手动进行转换以获得 cameras.txt ， images.txt 和 points3D.txt。三个文件各自的格式规定如下：

cameras.txt


1 PINHOLE 800 800 1111.1110311937682 1111.1110311937682 400.0 400.0

images.txt


1 0.0041 0.0056 -0.8064 0.5919 6.3306e-10 -5.1536e-08 4.0311 1 r_0.png

2 0.1086 0.15132 -0.7980 0.5729 4.9764e-08 -2.7316e-08 4.0311 1 r_1.png

3 0.5450 0.6810 -0.3817 0.3055 -1.2894e-07 -2.6036e-08 4.0311 1 r_10.png

points3D.txt


944 -0.3789 0.5152 -0.1104 58 65 88 0.0692 13 521 49 537 3 446
1054 -0.1167 -0.3606 -0.0849 180 176 187 0.2641 13 1285 49 1440 3 1307
5 -0.1028 -0.4174 0.8981 23 18 7 0.0205 65 33 1 23

完成该转换的 blender_camera2colmap.py 脚本内容如下：


import numpy as np
import json
import os
import imageio
import math

H = 800
W = 800

blender2opencv = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])

fnames = list(sorted(os.listdir('images')))
fname2pose = {}

with open('transforms_train.json', 'r') as f:
    meta = json.load(f)

fx = 0.5 * W / np.tan(0.5 * meta['camera_angle_x'])
if 'camera_angle_y' in meta:
    fy = 0.5 * H / np.tan(0.5 * meta['camera_angle_y'])
else:
    fy = fx
if 'cx' in meta:
    cx, cy = meta['cx'], meta['cy']
else:
    cx = 0.5 * W
    cy = 0.5 * H
with open('created/sparse/cameras.txt', 'w') as f:
    f.write(f'1 PINHOLE {W}{H}{fx}{fy}{cx}{cy}')
    idx = 1
    for frame in meta['frames']:
        fname = frame['file_path'].split('/')[-1]
        if not (fname.endswith('.png') or fname.endswith('.jpg')):
            fname += '.png'

        pose = np.array(frame['transform_matrix']) @ blender2opencv
        fname2pose.update({fname: pose})

with open('created/sparse/images.txt', 'w') as f:
    for fname in fnames:
        pose = fname2pose[fname]

        R = np.linalg.inv(pose[:3, :3])
        T = -np.matmul(R, pose[:3, 3])
        q0 = 0.5 * math.sqrt(1 + R[0, 0] + R[1, 1] + R[2, 2])
        q1 = (R[2, 1] - R[1, 2]) / (4 * q0)
        q2 = (R[0, 2] - R[2, 0]) / (4 * q0)
        q3 = (R[1, 0] - R[0, 1]) / (4 * q0)

        f.write(f'{idx}{q0}{q1}{q2}{q3}{T[0]}{T[1]}{T[2]} 1 {fname}\n\n')
        idx += 1

with open('created/sparse/points3D.txt', 'w') as f:
   f.write('')

直接在根目录 rootpath 下运行

python blender_camera2colmap.py

即可获得 created/sparse 文件下所需的内容。

三、COLMAP重建流程

1. 抽取图像特征

colmap feature_extractor --database_path database.db --image_path images

终端输出示例如下：

==============================================================================
Feature extraction
==============================================================================

Processed file [1/100]
  Name:            r_0.png
  Dimensions:      800 x 800
  Camera:
  Focal Length:    960.00px
  Features:        2403
Processed file [2/100]
  Name:            r_1.png
  Dimensions:      800 x 800
  Camera:
  Focal Length:    960.00px
  Features:        2865
Processed file [3/100]
..........

..........

Elapsed time: 0.075 [minutes]

2. 导入指定相机内参

前一步colmap获得了估计的相机内参，但我们有真实的相机内参，所以将colmap估出来的相机内参提环成我们自己的，使用的 transform_colmap_camera.py 脚本内容如下：


import sys
import numpy as np
import sqlite3

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 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 BLOB,
    config INTEGER NOT NULL,
    F BLOB,
    E BLOB,
    H BLOB,
    qvec BLOB,
    tvec 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 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 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 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 update_camera(self, model, width, height, params, camera_id):
        params = np.asarray(params, np.float64)
        cursor = self.execute(
            "UPDATE cameras SET model=?, width=?, height=?, params=?, prior_focal_length=1 WHERE camera_id=?",
            (model, width, height, array_to_blob(params),camera_id))
        return cursor.lastrowid

def camTodatabase(txtfile):
    import os
    import argparse

    camModelDict = {'SIMPLE_PINHOLE': 0,
                    'PINHOLE': 1,
                    'SIMPLE_RADIAL': 2,
                    'RADIAL': 3,
                    'OPENCV': 4,
                    'FULL_OPENCV': 5,
                    'SIMPLE_RADIAL_FISHEYE': 6,
                    'RADIAL_FISHEYE': 7,
                    'OPENCV_FISHEYE': 8,
                    'FOV': 9,
                    'THIN_PRISM_FISHEYE': 10}
    parser = argparse.ArgumentParser()
    parser.add_argument("--database_path", default="database.db")
    args = parser.parse_args()
    if os.path.exists(args.database_path)==False:
        print("ERROR: database path dosen't exist -- please check database.db.")
        return

    db = COLMAPDatabase.connect(args.database_path)

    idList=list()
    modelList=list()
    widthList=list()
    heightList=list()
    paramsList=list()

    with open(txtfile, "r") as cam:
        lines = cam.readlines()
        for i in range(0,len(lines),1):
            if lines[i][0]!='#':
                strLists = lines[i].split()
                cameraId=int(strLists[0])
                cameraModel=camModelDict[strLists[1]]
                width=int(strLists[2])
                height=int(strLists[3])
                paramstr=np.array(strLists[4:12])
                params = paramstr.astype(np.float64)
                idList.append(cameraId)
                modelList.append(cameraModel)
                widthList.append(width)
                heightList.append(height)
                paramsList.append(params)
                camera_id = db.update_camera(cameraModel, width, height, params, cameraId)

    db.commit()

    rows = db.execute("SELECT * FROM cameras")
    for i in range(0,len(idList),1):
        camera_id, model, width, height, params, prior = next(rows)
        params = blob_to_array(params, np.float64)
        assert camera_id == idList[i]
        assert model == modelList[i] and width == widthList[i] and height == heightList[i]
        assert np.allclose(params, paramsList[i])

    db.close()

if __name__ == "__main__":
    camTodatabase("created/sparse/cameras.txt")

直接在根目录 rootpath 下运行

python transform_colmap_camera.py

即可完成database.db中相机内参的替换。

3. 特征匹配

colmap exhaustive_matcher --database_path database.db

终端输出示例如下：

==============================================================================
Exhaustive feature matching
==============================================================================

Matching block [1/2, 1/2] in 5.688s
Matching block [1/2, 2/2] in 5.234s
Matching block [2/2, 1/2] in 5.609s
Matching block [2/2, 2/2] in 5.165s
Elapsed time: 0.364 [minutes]

4. 三角测量

colmap point_triangulator --database_path database.db --image_path images --input_path created/sparse --output_path triangulated/sparse

终端输出示例如下：

==============================================================================
Loading model
==============================================================================
==============================================================================
Loading database
==============================================================================
Loading cameras... 100 in 0.000s
Loading matches... 1330 in 0.003s
Loading images... 100 in 0.012s (connected 100)
Building correspondence graph... in 0.025s (ignored 0)
Elapsed time: 0.001 [minutes]

==============================================================================
Triangulating image
==============================================================================
 => Image sees 0 / 465 points
 => Triangulated 284 points
..........

..........

Bundle adjustment report
ref_image_idx: 0
src_image_idxs: 20 36 80 64 37 61 1 73 58 32 47 19 3 46 57 4 77 53 28 33

PatchMatchOptions
Initialization: 0.1131s
 Sweep 1: 0.4373s
 Sweep 2: 0.3998s
 Sweep 3: 0.4118s
 Sweep 4: 0.3944s
Iteration 1: 1.6447s
 Sweep 1: 0.4101s
 Sweep 2: 0.3992s
..........

..........

Writing geometric output for r_99.png
Elapsed time: 61.006 [minutes]

7. 稠密点云融合

colmap stereo_fusion --workspace_path dense --output_path dense/fused.ply

终端输出示例如下：

`bash
StereoFusion::Options

Original: https://blog.csdn.net/qq_38677322/article/details/126269726
Author: zParquet
Title: COLMAP利用已知相机内外参重建NeRF的blender模型

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