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    Robbyant

    Robbyant/lingbot-map

    #94 this week

    [ECCV 2026 Best Paper Award Candidate] LingBot-Map: Geometric Context Transformer for Streaming 3D Reconstruction

    backend
    Python
    Apache-2.0
    18.1K stars
    2.0K forks
    18.1K GitHub watchers
    Updated Oct 14, 2026
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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    Ranks #94 of 2,503 repositories GStars tracks this week (top 4%).

    The project lists 3 contributors on GitHub.

    Peaked at #3 in the weekly ranking the week of Jul 12, its best in the past year.

    Use Cases & Benefits

    • Implements a geometric context transformer for efficient streaming 3D reconstruction from video sequences.
    • Enables stable, high-speed inference on long sequences using a feed-forward architecture with paged KV cache attention.
    • Use for real-time 3D mapping and reconstruction in robotics or autonomous navigation with continuous video input.
    • Use for offline batch rendering of large-scale indoor or outdoor scenes from long video walkthroughs.
    • Use for research and benchmarking on diverse 3D reconstruction datasets like KITTI, Oxford Spires, and Tanks and Temples.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    Apache 2.0

    Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.

    Community files

    • README: present

    Watch-outs

    • No GitHub releases; pin a commit or tag instead of a release.

    General guidance from repository metadata, not legal advice. How we score

    From the README

    PyTorch 2.8.0 is the recommended version because NVIDIA Kaolin (required by the batch rendering pipeline) has prebuilt wheels for torch-2.8.0_cu128. If you only need demo.py you may use a newer PyTorch, but the batch renderer then requires building Kaolin from source. For other CUDA versions, see PyTorch Get Started.

    FlashInfer provides paged KV cache attention for efficient streaming inference. It is a pure-Python package that JIT-compiles CUDA kernels on first use, so a single wheel works across CUDA/PyTorch versions:

    --index-url https://pypi.org/simple is only needed if your default pip index is an internal mirror that doesn't have flashinfer-python. (Optional) For faster first-use, you can additionally install a CUDA-specific JIT cache: pip install flashinfer-jit-cache -f https://flashinfer.ai/whl/cu128/flashinfer-jit-cache/. See FlashInfer installation for details. If FlashInfer is not installed, the model falls back to SDPA (PyTorch native attention) via --use_sdpa.

    🚧 Coming soon: we're training an stronger model that supports longer sequences — stay tuned.

    Excerpt from the Robbyant/lingbot-map README on GitHub (Apache-2.0 license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Jul 18: +764Jul 19: +827Jul 20: +554Jul 21: +574Jul 22: +205Jul 23: +191Jul 24: +191Jul 25: +123Jul 26: +147Jul 27: +147Jul 28: +90Jul 29: +85Jul 30: +79Jul 31: +70Aug 1: +32Aug 2: +30Aug 3: +43Aug 4: +47Aug 5: +83Aug 6: +41Aug 7: +40Aug 8: +25Aug 9: +18Aug 10: +18Aug 11: +46Aug 12: +32Aug 13: +18Aug 14: +16Aug 15: +7Oct 10: +310Oct 11: +54Oct 12: +17Oct 13: +39Oct 14: +30

    +6,126 stars over 89 days across 39 snapshot days; best day +827 on Jul 19.

    Forks

    2.0K+720

    Open issues

    85+22

    Compared with peers in Backend

    Fastest-growing repositories GStars tracks in this cohort for the week of Oct 11, by 7-day star growth

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