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H3DNAS: Hardware-Aware ONNX-Native 3D Point Cloud Model Compression

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

Deploying 3D point cloud models on edge hardware such as the NVIDIA Jetson Orin Nano is severely constrained by compute and memory budgets. Existing compression methods require access to the model's original source code, rendering them inapplicable to the Open Neural Network Exchange (ONNX) binaries commonly distributed by vendors and model repositories. We present \textbf{H3DNAS}, a hardware-aware model compression framework that operates directly on ONNX computational graphs without requiring original source code, architecture class definition, or gradient access during search. H3DNAS makes

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Evidence & attribution

First collected: 2026-09-21T05:32:15.665Z. This is not the publication date.