Colmap Vocabulary Tree Download ##BEST##
Princesa Landes
The source code is available on GitHub and the documentation is available at colmap.github.io. Please, use the Google Group (col...@googlegroups.com) for questions and the GitHub issue tracker for bug reports, feature requests/additions, etc.
The first step is to start the graphical user interface of COLMAP by running thepre-built binaries (Windows: COLMAP.bat, Mac: COLMAP.app) or by executing./src/colmap/exe/colmap gui from the CMake build folder. Next, create a new projectby choosing File > New project. In this dialog, you must select where tostore the database and the folder that contains the input images. Forconvenience, you can save the entire project settings to a configuration file bychoosing File > Save project. The project configuration stores the absolutepath information of the database and image folder in addition to any otherparameter settings. If you decide to move the database or image folder, you mustchange the paths accordingly by creating a new project. Alternatively, theresulting .ini configuration file can be directly modified in a text editor ofyour choice. To reopen an existing project, you can simply open theconfiguration file by choosing File > Open project and all parametersettings should be recovered. Note that all COLMAP executables can be startedfrom the command-line by either specifying individual settings as command-linearguments or by providing the path to the project configuration file (seeInterface).
Sequential Matching: This mode is useful if the images are acquired insequential order, e.g., by a video camera. In this case, consecutive frameshave visual overlap and there is no need to match all image pairsexhaustively. Instead, consecutively captured images are matched againsteach other. This matching mode has built-in loop detection based on avocabulary tree, where every N-th image (loop_detection_period) is matchedagainst its visually most similar images (loop_detection_num_images). Notethat image file names must be ordered sequentially (e.g., image0001.jpg,image0002.jpg, etc.). The order in the database is not relevant, since theimages are explicitly ordered according to their file names. Note that loopdetection requires a pre-trained vocabulary tree, that can be downloadedfrom
Vocabulary Tree Matching: In this matching mode [schoenberger16vote],every image is matched against its visual nearest neighbors using a vocabularytree with spatial re-ranking. This is the recommended matching mode for largeimage collections (several thousands). This requires a pre-trained vocabularytree, that can be downloaded from
Perform additional matching. For best results, use exhaustive matching, enableguided matching, increase the number of nearest neighbors in vocabulary treematching, or increase the overlap in sequential matching, etc.
vocab_tree_builder: Create a vocabulary tree from a database withextracted images. This is an offline procedure and can be run once, while thesame vocabulary tree can be reused for other datasets. Note that, as a rule ofthumb, you should use at least 10-100 times more features than visual words.Pre-trained trees can be downloaded from is useful if you want to build a custom tree with a different trade-offin terms of precision/recall vs. speed.
It basicly use the same idea as bag-of-word used in NLP(nature language processing), except we treat the visual features as words, named visual words. Each image is a group of visual words, and using the vocabulary to compress the image into a vector. With the vectors and defined distance, we can find the nearest neighbour in the image data base.
They all used Colmap pipeline as basic, and used deep learning mehtod to upate the features(add semantic labels / deep learning extraction method). As a result, we choose to go with the same direction : start from colmap structure, and use deep learning to help updating.
Match with image database. Possible choice: BOW, Exhaustive, Deep learning( NetVLAD ). Or Match with whole map/local map.Exhaustive match will be too slow for our real time application, so we choost to use vocabulary tree method (BOW).
Additionally, the tracked features in the new keyframe are matched with existing 3D points in the map using a vocabulary-based approach [34]. The use of a feature vocabulary reduces the amount of processing required to match points compared to brute-forced approach. This is the same matching method we use for relocalization, which we describe further in Sect. 4.8.4.
Active search [43] uses a bi-directional feature matching method. First, a descriptor vocabulary is used to quantize the descriptor space, and words are assigned to each point in the model as well as to each feature in the query image. For each feature in the query image, the 3D points which share a node in the vocabulary tree are searched for matches using the typical ratio test [44] resulting in an initial match to a point .
Then, the 3D points in the neighborhood of this match are prioritized and matched to the features in the inverse direction using a coarser vocabulary. The purpose of this bi-directional matching is to make use of the fact that points in the same 3D region are likely to share similar visibility.
We also require a vocabulary file containing representative ORB descriptors and use the one provided with ORB-SLAM2 [47]. The BoW approach allows for quick matching between images using an inverted file that contains both image and keypoint indices for each word. We investigate the feasibility of fast image retrieval using ORB, while maintaining robust matching via SIFT.
Pre-processing This stage only needs to be completed once per COLMAP model. The objective of this phase is to create an inverted index file, which stores for each word in the vocabulary, a list of image identifiers corresponding to the training images that contain that word. The purpose of this file is to act as a database for image retrieval and only needs to be computed once per SfM model.
We first detect 2000 ORB features in each training image and then map the descriptors to words in the vocabulary using the optimized transformation implementation of fbow [34]. Finally, the inverted index is updated by appending the image identifier to the corresponding list in the inverted file for all transformed words in the image.
Localization To localize a query image, we first detect and map ORB features to the vocabulary as before. Then for each word in the query image, we parse the list of training images via the inverted file and accumulate votes for each training image that contains that descriptor. The image with the most votes is accepted as the closest match, from which we begin establishing matches to compute a 6-DoF pose.
For ESAC, the training time includes initializing the gating network (classifier), initializing and refining 4 experts, and the end-to-end training stage. For Active Search, this includes the pre-processing stages of parsing the SfM data and computing descriptor assignments with the vocabulary. For our BoW method, this includes all steps detailed under pre-processing in Sect. 4.8.4, namely ORB detection, fbow transformation and creation of the inverted index.
One apparent limitation of our system is that we can see three instances where tracking appears to become less robust after the relocalization point in Court B and C. While both approaches could successfully relocalize, our approach lost tracking for some frames in some instances. This could be due to the lack of matches to the existing map points after relocalization. During tracking, and before a tracking failure, our system is usually able to maintain many 2D-to-3D matches aided by the robustness of the KLT tracking. However, after a tracking failure fewer matches could be re-established using our vocabulary tree-based relocalization approach. For future work, this could be improved such as by using an Active search [55]-based relocalization approach to establish more correspondences.
In order to run MultiNeRF on your own captured images of a scene, you must first run COLMAP to calculate camera poses. You can do this using our provided script scripts/local_colmap_and_resize.sh. Just make a directory my_dataset_dir/ and copy your input images into a folder my_dataset_dir/images/, then run:bash scripts/local_colmap_and_resize.sh my_dataset_dirThis will run COLMAP and create 2x, 4x, and 8x downsampled versions of your images. These lower resolution images can be used in NeRF by setting, e.g., the Config.factor = 4 gin flag.
By default, local_colmap_and_resize.sh uses the OPENCV camera model, which is a perspective pinhole camera with k1, k2 radial and t1, t2 tangential distortion coefficients. To switch to another COLMAP camera model, for example OPENCV_FISHEYE, you can runbash scripts/local_colmap_and_resize.sh my_dataset_dir OPENCV_FISHEYE
If you have a very large capture of more than around 500 images, we recommend switching from the exhaustive matcher to the vocabulary tree matcher in COLMAP (see the script for a commented-out example).
Structure-based methods use feature matching between query image and map to obtain the 6DoF pose. The 3D map is mostly constructed by SfM methods [11,24,25]. The pose of image query is computed by matching key points in the query image and 3D points in the 3D map, and then solving the Perspective-n-Points (PnP) problem. In this type of method, in addition to traditional pose calculating methods, most methods also need to use labeled data for training. For example, DSAC (differentiable RANSAC (Random Sample Consensus)) [26] and DSAC++ [27] need RGB-D data, and BTBRF (Backtracking Regression Forests for Accurate Camera Relocalization) [28] also needs camera pose for training. However, the searching and matching calculations increase with the points increase in the 3D map. In order to improve the efficiency of such approaches, researchers have proposed several solutions including vocabulary trees [29], prioritized searches [30], and remote server calculations [31]. However, the benefits of these methods are limited and they are not suitable for resource-constrained mobile platforms and large-scale scenarios. In addition, local features lack the ability to capture the global context of the image and require the robust aggregation of points to effectively achieve pose estimation.
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