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English(EN) H3DNAS: Hardware-Aware ONNX-Native 3D Point Cloud Model Compression

新的 H3DNAS 框架在 ONNX 二进制文件上压缩 3D 点云模型

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了 H3DNAS，一个新颖的 3D 点云模型压缩框架，可以直接在 ONNX 二进制文件上运行，无需原始源代码。该方法通过在分布式 ONNX 文件上进行压缩，解决了在 NVIDIA Jetson Orin Nano 等边缘硬件上部署模型的局限性。H3DNAS 利用通道依赖图和两阶段分层搜索来减少模型参数，并在精度损失最小的情况下提高推理速度。 AI

影响 能够更有效地在资源受限的边缘设备上部署 3D 点云模型。

排序理由 该集群描述了一篇详细介绍新颖模型压缩方法的最新研究论文。

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的 H3DNAS 框架在 ONNX 二进制文件上压缩 3D 点云模型

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该集群描述了一篇详细介绍新颖模型压缩方法的最新研究论文。
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报道来源 [2]

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

    H3DNAS：硬件感知、ONNX 原生 3D 点云模型压缩

    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：硬件感知型 ONNX 原生 3D 点云模型压缩

    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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