[P] A lightweight FoundationPose TensorRT implementation
After being frustrated with the official FoundationPose codebase for my robotics research, I built a lightweight TensorRT implementation and wanted to share it with the community.
The core is based on model code from tao-toolkit-triton-apps, but with the heavy Triton Inference Server dependency completely removed in favor of a direct TensorRT backend. For the ONNX models, I use the ones from isaac_ros_foundationpose, since I ran into issues with the officially provided ones. So essentially it’s those two sources combined with a straightforward TensorRT backend.
Some highlights:
- Reduced VRAM usage – You can shrink the input layer of the network, lowering VRAM consumption while still running the standard 252 batch size by splitting inference into smaller sequential batches.
- Minimal dependencies – All you need is CUDA Toolkit + TensorRT (automatically set up via a script I provide) + a Python environment with a handful of packages.
I spent a long time looking for something like this without luck, so I figured some of you might find it useful too.
submitted by /u/seawee1
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