Bavantha11 / vio-stack-jetson-weights

M2H-MX ONNX weights — monocular depth + semantics

Exported ONNX weights for the two M2H-MX networks used by vio_stack_jetson , a monocular VIO stack running on a Jetson Orin with a ZED X One GS camera.

Both networks take a single RGB frame and produce metric depth and per-pixel semantic labels in one pass. Both publish the same two ROS 2 topics, so they are swappable at runtime:

/m2h/depth/image             32FC1 metric depth
/m2h/semantic/labels_argmax  mono8 class IDs

Built with DINOv3.

Files

The layout mirrors the ROS packages that consume these files, so a manifest line maps to a path with no translation:

MANIFEST.sha256 in each package directory lists the expected SHA256 of every file. Verify after downloading — a network running on the wrong weights produces plausible-looking depth rather than an error, which is much harder to notice than a refusal to start.

Usage

git clone https://github.com/BavanthaU/vio_stack_jetson
cd vio_stack_jetson
./tools/fetch_weights.sh          # pulls these files and verifies every sum

Or directly:

from huggingface_hub import snapshot_download
snapshot_download(repo_id="Bavantha11/vio-stack-jetson-weights", repo_type="model")

Measured on the rig

Jetson Orin, JetPack r36.5, ONNX Runtime with the TensorRT execution provider:

The first run at any resolution builds a TensorRT engine — minutes, once, cached per machine. The engines are not portable across machines or driver versions and are deliberately not published here.

Depth is not fed back into the VIO filter by default; it runs alongside it.

Architecture

• Backbone: DINOv3 ViT-B/16 ( dinov3-vitb16-pretrain-lvd1689m )
• Heads: two — semantic segmentation (40 classes) and metric depth
• Opset: 17, static shapes
• Training data: ScanNet, NYUDv2

Licensing — read before use

These weights combine inputs with different terms. The most restrictive one governs the result.

DINOv3 backbone — DINOv3 License , included here as DINOv3_LICENSE.md . It permits commercial use, modification and redistribution of derivative works, and asks in return that redistributions carry a copy of the licence and display "Built with DINOv3". Both are satisfied above.

ScanNet — Terms of Use . Non-commercial research and educational use only , and those terms extend to derivative works.

NYUDv2 — research use.

Therefore these weights are for non-commercial research and educational use only. ScanNet is the binding constraint; DINOv3 permitting commercial use does not lift it. Anyone needing a commercial deployment must retrain on commercially licensed data — the ROS packages and the export pipeline are MIT and unaffected.

The MIT licence on the m2h_mx_* ROS packages covers the wrapper code only , not these weights.

Citation

If you use these in published work, please acknowledge DINOv3 and cite the ScanNet and NYUDv2 datasets as their terms require.