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This is a C++ code example of using the OpenCV rgbd module to implement the LINE-MOD algorithm for 6D pose estimation of textured or texture-less objects. The code shows how to train and test the LINE-MOD detector on different datasets, such as LINEMOD, BOP, and GenMOP.


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A tool for self-made Linemod dataset, used for pose estimation algorithms. - GitHub - HaofeiMa/Linemod_Custom: A tool for self-made Linemod dataset, used for pose estimation algorithms.


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The pipeline implements the OpenCV LINE-MOD Detector to estimate the pose of a known object. Postprocessing steps for detection of shiny objects with corrupted depth information are added. The goal of this pipeline is to locate and detect the pose of industrial objects like bearings to aide a robotic arm during assembly (bin picking).


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An OR pipeline based on LINE-MOD from OpenCV. Contribute to wg-perception/linemod development by creating an account on GitHub.


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Modality that computes quantized surface normals from a dense depth map. More. class. cv::linemod::Detector. Object detector using the LINE template matching algorithm with any set of modalities. More. struct. cv::linemod::Feature. Discriminant feature described by its location and label. More.


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Execute the Linemod in the training mode with the configuration file through the -c option. The configuration file should define a pipeline that reads data from the database and computes objects models. rosrun object_recognition_core training -c ` rospack find object_recognition_linemod ` /conf/training.ork.


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The current state-of-the-art on LineMOD is RNNPose. See a full comparison of 21 papers with code.


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The LINE-MOD method primarily relies on discrimina-tive gradient features. More specifically, a gradient image is computed for each color channel, and at a specific loca-tion on the image, the algorithm selects the gradient orien-tation with the largest magnitude among 3 color channels.


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steps. in target folder: mkdir build. cd build/. cmake .. make. in top level folder, if use pybind: pip3 install target_folder/. python3 patch_linemod_test.py.


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Modality that computes quantized surface normals from a dense depth map. More. Object detector using the LINE template matching algorithm with any set of modalities. More. Discriminant feature described by its location and label. More. Represents a successful template match.