6D Object Pose Estimation using 3D Object Coordinates [Data]
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Supplementary training data and binaries for 6D object pose estimation, particularly a dataset of 20 objects under various lighting conditions with RGB-D images, ground truth poses and segmentation as well as 3D models. Additionally, a collection of RGB-D images showing office backgrounds. We also provide additional pose annotations of the LINEMOD dataset (http://campar.in.tum.de/Main/StefanHinterstoisser), and Linux binaries to reproduce the results of our paper.For more information, also see the project page: https://hci.iwr.uni-heidelberg.de/vislearn/research/scene-understanding/pose-estimation/#ECCV1' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Computer Vision","Machine Learning"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Supplementary training data and binaries for 6D object pose estimation, particularly a dataset of 20 objects under various lighting conditions with RGB-D images, ground truth poses and segmentation as well as 3D models. Additionally, a collection of RGB-D images showing office backgrounds. We also provide additional pose annotations of the LINEMOD dataset (http://campar.in.tum.de/Main/StefanHinterstoisser), and Linux binaries to reproduce the results of our paper.For more information, also see the project page: https://hci.iwr.uni-heidelberg.de/vislearn/research/scene-understanding/pose-estimation/#ECCV1' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Supplementary training data and binaries for 6D object pose estimation, particularly a dataset of 20 objects under various lighting conditions with RGB-D images, ground truth poses and segmentation as well as 3D models. Additionally, a collection of RGB-D images showing office backgrounds. We also provide additional pose annotations of the LINEMOD dataset (http://campar.in.tum.de/Main/StefanHinterstoisser), and Linux binaries to reproduce the results of our paper.For more information, also see the project page: https://hci.iwr.uni-heidelberg.de/vislearn/research/scene-understanding/pose-estimation/#ECCV1
Computer Vision; Machine Learning