COLMAP stores all extracted information in a single SQLite database file. The
database can be accessed with the database management toolkit in the COLMAP GUI,
the provided C++ database API (see
src/base/database.h), or with a scripting
language of your choice (see
The database contains the following tables:
To initialize an empty SQLite database file with the required schema, you can either create a new project in the GUI or execute src/exe/database_create.cc.
Cameras and Images¶
The relation between cameras and images is 1-to-N. This has important implications for Structure-from-Motion, since one camera shares the same intrinsic parameters (focal length, principal point, distortion, etc.), while every image has separate extrinsic parameters (orientation and location).
The intrinsic parameters of cameras are stored as contiguous binary blobs in
float64, ordered as specified in
src/base/camera_models.h. COLMAP only
uses cameras that are referenced by images, all other cameras are ignored.
name column in the images table is the unique relative path in the image
folder. As such, the database file and image folder can be moved to different
locations, as long as the relative folder structure is preserved.
When manually inserting images and cameras into the database, make sure
that all identifiers are positive and non-zero, i.e.
image_id > 0
camera_id > 0.
Keypoints and Descriptors¶
The detected keypoints are stored as row-major float32 binary blobs, where the
first two columns are the X and Y locations in the image, respectively. COLMAP
uses the convention that the upper left image corner has coordinate (0, 0) and
the center of the upper left most pixel has coordinate (0.5, 0.5). If the
keypoints have 4 columns, then the feature geometry is a similarity and the
third column is the scale and the fourth column the orientation of the feature
(according to SIFT conventions). If the keypoints have 6 columns, then the
feature geometry is an affinity and the last 4 columns encode its affine shape
src/feature/types.h for details).
The extracted descriptors are stored as row-major uint8 binary blobs, where each row describes the feature appearance of the corresponding entry in the keypoints table. Note that COLMAP only supports 128-D descriptors for now, i.e. the cols column must be 128.
In both tables, the rows table specifies the number of detected features per image, while rows=0 means that an image has no features. For feature matching and geometric verification, every image must have a corresponding keypoints and descriptors entry. Note that only vocabulary tree matching with fast spatial verification requires meaningful values for the local feature geometry, i.e., only X and Y must be provided and the other keypoint columns can be set to zero. The rest of the reconstruction pipeline only uses the keypoint locations.
Feature matching stores its output in the matches table and geometric verification in the inlier_matches table. COLMAP only uses the data in inlier_matches for reconstruction. Every entry in the two tables stores the feature matches between two unique images, where the pair_id is the row-major, linear index in the upper-triangular match matrix, generated as follows:
def image_ids_to_pair_id(image_id1, image_id2): if image_id1 > image_id2: return 2147483647 * image_id2 + image_id1 else: return 2147483647 * image_id1 + image_id2
and image identifiers can be uniquely determined from the pair_id as:
def pair_id_to_image_ids(pair_id): image_id2 = pair_id % 2147483647 image_id1 = (pair_id - image_id2) / 2147483647 return image_id1, image_id2
The pair_id enables efficient database queries, as the matches tables may contain several hundred millions of entries. This scheme limits the maximum number of images in a database to 2147483647 (maximum value of signed 32-bit integers), i.e. image_id must be smaller than 2147483647.
The binary blobs in the matches tables are row-major uint32 matrices, where the left column are zero-based indices into the features of image_id1 and the second column into the features of image_id2. The column cols must be 2 and the rows column specifies the number of feature matches.