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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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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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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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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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opencv 3.3 python linemod: how to add templates. today it try to use the linemod algorithm which is part of OpenCV. In an earlier C++ project I already use linemod and it works like it is described in the documentation, but this time I try to use the Python API. import cv2 template = cv2.imread ('path_to_template') lineModDetector = cv2.linemod.
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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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The current state-of-the-art on LineMOD is RNNPose. See a full comparison of 21 papers with code.
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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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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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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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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.
