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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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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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Description Place props, trees, and/or buildings in a line Still under active and comparatively early development, but already does all the required basics; the UI in particular is a bit under-developed in this version, and will be improved via updates. Incompatible mods
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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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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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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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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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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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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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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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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.