How to Make the Best Self-Driving Dataset

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  1. Self-Driving-Dataset-Workshop

    Hey there! Dan Gural here from Voxel51, and I recently gave an in-person workshop on How to Unlock More in Self-Driving Datasets. I’m super excited to dive into this with you today and bring it to everyone! I highly recommend checking out my video where I dive into detail about the workshop, and to check out the GitHub repo where all the materials are stored.

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    CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.

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  3. pytorch3d

    PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

    Hardware isn’t the only area seeing leaps forward. Open-source libraries have become much better too. A few years ago, setting up tools like PyTorch 3D was a nightmare. Today, it takes hours, not days, thanks to optimization and community support. These advancements have streamlined workflows and allowed for faster experimentation.

  4. fiftyone

    Refine high-quality datasets and visual AI models

    But the challenge lies in organizing and managing this unstructured data. That’s where tools like FiftyOne, Voxel 51’s open-source dataset management tool, come in.

  5. drivestudio

    A 3DGS framework for omni urban scene reconstruction and simulation.

    With tools like DriveStudio and Gaussian Splats, researchers are building 3D environments where they can simulate real-world driving conditions without actually being on the road. This opens up a whole new world of possibilities for testing, validating, and improving self-driving models in a fraction of the time.

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