@@ -7,7 +7,7 @@ The datasets for the 8 scenarios are currently available in compressed zip files
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@@ -7,7 +7,7 @@ The datasets for the 8 scenarios are currently available in compressed zip files
Detailed information regarding the dataset generation process can be accessed through the following links: "https://gitlab.liris.cnrs.fr/gduret/PickSim" for insights into the PickSim generation, and this repositiry for access to the processing codes and procedures involved in creating FruitBin, as well as the scenarios designed for training purposes. These resources provide valuable documentation and code references for a better understanding of the dataset's origins and the steps taken to prepare it for various applications.
Detailed information regarding the dataset generation process can be accessed through the following links: "https://gitlab.liris.cnrs.fr/gduret/PickSim" for insights into the PickSim generation, and this repositiry for access to the processing codes and procedures involved in creating FruitBin, as well as the scenarios designed for training purposes. These resources provide valuable documentation and code references for a better understanding of the dataset's origins and the steps taken to prepare it for various applications.
To facilitate the reproduction of the benchmark results, we have provided the code for PVnet and Densefusion along with detailed instructions on how to apply them to the FruitBin dataset. You can access the code at the following links: "https://gitlab.liris.cnrs.fr/gduret/pvnet_fruitbin" and "https://gitlab.liris.cnrs.fr/gduret/DenseFusion". These resources will guide you through the implementation of pvnet and Densefusion on the FruitBin dataset, enabling you to replicate the benchmark and further explore the capabilities of the dataset.
To facilitate the reproduction of the benchmark results, we have provided the code for PVNet, Densefusion and GDRNPP along with detailed instructions on how to apply them to the FruitBin dataset. You can access the code at the following links: "https://gitlab.liris.cnrs.fr/gduret/pvnet_fruitbin", "https://gitlab.liris.cnrs.fr/gduret/DenseFusion" and "https://gitlab.liris.cnrs.fr/gduret/gdrnpp_bop2022". These resources will guide you through the implementation of PVNet, Densefusion and GDRNPP on the FruitBin dataset, enabling you to replicate the benchmark and further explore the capabilities of the dataset.
## Getting started
## Getting started
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@@ -210,8 +210,12 @@ The following table present the different input parameters :
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@@ -210,8 +210,12 @@ The following table present the different input parameters :
The preprocessing for DenseFusion is now complete. For more detailed information, please refer to the following link: https://gitlab.liris.cnrs.fr/gduret/DenseFusion .
The preprocessing for DenseFusion is now complete. For more detailed information, please refer to the following link: https://gitlab.liris.cnrs.fr/gduret/DenseFusion .
# Compute step for GDRNPP
The preprocessing part to obtain BOP format of FruitBin are detailled in our GDRNPP folder: "https://gitlab.liris.cnrs.fr/gduret/gdrnpp_bop2022"
## Authors and acknowledgment
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
## License
## License
The dataset is licensed under the CC BY-NC-SA license.
The dataset is licensed under the CC BY-NC-SA license.
