Hello,
I recently read the paper titled "Stereo Plane R-CNN: Accurate Scene Geometry Reconstruction Using Planar Segments and Camera-Agnostic Representation" published in IEEE Robotics and Automation Letters (RA-L). I found this work highly interesting and would like to explore it further by reproducing the experiments.
However, I encountered an issue when trying to access the datasets and pretrained model provided in the GitHub repository—the links are no longer accessible. Could you kindly provide updated links or share the datasets (SceneNet Stereo and TERRINet) and pretrained model through an alternative method?
Your assistance would be greatly appreciated, as it would enable me to fully understand and build upon your work.
Thank you in advance!
梦欢 Okay
The availability of datasets and pretrained models mentioned in research papers often depends on the authors' permissions, licensing, or third-party agreements. In the case of "Stereo Plane R-CNN," it appears the GitHub links for the SceneNet Stereo and TERRINet datasets, as well as the pretrained model, are currently inaccessible. Here’s a step-by-step guide on what you can do to resolve this:
1. Contact the Authors
Reach out to the authors via the email addresses provided in the paper. They are often more than willing to assist researchers interested in replicating their work. Mention your interest and specify the issues with the dataset and model links.
Example: "Imagine you're borrowing notes from a senior classmate for exam preparation—clear communication is key. Similarly, emailing the authors clearly explaining your intention (reproduction of results) can help them provide access or guide you to the right resources."
2. Search for Dataset and Model Alternatives
Sometimes datasets like SceneNet Stereo and TERRINet are hosted on alternative platforms. Use academic data repositories like IEEE DataPort, Google Dataset Search, or similar platforms.
Think of this as looking for a misplaced book in a library. The book might not be on the usual shelf, but checking with the librarian or the catalog can lead you to the correct section.
3. Community and Forums
Check research discussion platforms such as:
- Reddit (e.g., r/MachineLearning) for community-shared resources.
- GitHub Issues Section of the Stereo Plane R-CNN repository.
- ResearchGate or similar forums where fellow researchers might share updated links.
It’s like asking your classmates where the notes are—someone might already have the answer.
4. Replicating Models
If all else fails, consider re-training the model using the same methods mentioned in the paper. While it’s resource-intensive, it might be a learning opportunity to explore how the model behaves under various conditions.
Think of it like cooking a dish—when the recipe is unclear, experimenting yourself might yield surprising results!
#Robotics #StereoVision #DatasetAccess #ResearchSupport
Thank you so much for your detailed and helpful response! I truly appreciate the time and effort you put into providing such clear guidance. Following your advice, I have already reached out to the authors via email and am currently trying to train the model using my own dataset.@Qamar Ul Islam
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