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New H3DNAS framework compresses 3D point cloud models on ONNX binaries

By PulseAugur Editorial · [2 sources] ·

Researchers have developed H3DNAS, a novel framework for compressing 3D point cloud models that operates directly on ONNX binaries without needing original source code. This method addresses the limitations of deploying models on edge hardware like the NVIDIA Jetson Orin Nano by enabling compression on distributed ONNX files. H3DNAS utilizes a Channel Dependency Graph and a Two-Stage Hierarchical Search to reduce model parameters and improve inference speed with minimal accuracy loss. AI

IMPACT Enables more efficient deployment of 3D point cloud models on resource-constrained edge devices.

RANK_REASON The cluster describes a new research paper detailing a novel method for model compression.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New H3DNAS framework compresses 3D point cloud models on ONNX binaries

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The cluster describes a new research paper detailing a novel method for model compression.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Anchit Mulye, Rhythm Baghel, Sujay Kumar Ingle, Hardik Jain ·

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

    arXiv:2609.02684v1 Announce Type: new Abstract: 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 t…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Hardik Jain ·

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

    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 Exch…

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