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@@ -14,6 +14,7 @@ Symmetry Annotations: Each object is annotated with one or more symmetry axes, c
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  Applications: Ideal for use in pose estimation tasks, symmetry-aware machine learning models, and 3D object analysis.
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  ## Current Status:
 
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  Subset Selection: We have screened approximately 7000 objects from the ShapeNetV2 dataset (out of a total of 55,000 objects).
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  Symmetry Annotations: Only a portion of these 7000 objects currently includes symmetry axis annotations. The remaining models will be annotated and uploaded over time.
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  Future Work: The symmetry axis annotations for the rest of the selected models are expected to be completed by the end of 2024.
@@ -25,6 +26,7 @@ This dataset is built upon the ShapeNetV2 dataset, which contains richly annotat
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  Original ShapeNetV2 dataset can be accessed here: https://www.paris.inria.fr/archive_ylabbeprojectsdata/megapose/tars/shapenetcorev2.zip
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  ## 3. Dataset Structure
 
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  The dataset is organized as follows:
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  Models: 3D models from ShapeNetV2 in .obj format.
 
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  Applications: Ideal for use in pose estimation tasks, symmetry-aware machine learning models, and 3D object analysis.
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  ## Current Status:
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+
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  Subset Selection: We have screened approximately 7000 objects from the ShapeNetV2 dataset (out of a total of 55,000 objects).
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  Symmetry Annotations: Only a portion of these 7000 objects currently includes symmetry axis annotations. The remaining models will be annotated and uploaded over time.
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  Future Work: The symmetry axis annotations for the rest of the selected models are expected to be completed by the end of 2024.
 
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  Original ShapeNetV2 dataset can be accessed here: https://www.paris.inria.fr/archive_ylabbeprojectsdata/megapose/tars/shapenetcorev2.zip
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  ## 3. Dataset Structure
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  The dataset is organized as follows:
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  Models: 3D models from ShapeNetV2 in .obj format.