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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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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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62.3. SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation. Enter. 2021. 2. RNNPose. ( Trained with synthetic data and LINEMOD training set, w/o pbr data) 60.65. RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization.
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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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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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LINE-MOD [25] algorithm is one of the state-of-the-art methods for generic rigid object recognition using very fast template matching method. It employs 2D gradients and 3D surface normals for object detection. The algorithm is proved to be fast and robust even under heavily cluttered environment.
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Why erode the mask? Why not just leave it what it is and take all the gradients anywhere on the object like Hinterstoisser et al. originally proposed in their paper? (As I understood it, LINE-MOD/LINE-2D is for detecting textureless object, not for detecting silhouttes of objects.)
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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 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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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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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.
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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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