无需源码，直接压缩ONNX格式3D点云模型，适配边缘设备。
H3DNAS: Hardware-Aware ONNX-Native 3D Point Cloud Model Compression
- 基于ONNX图结构的通道依赖分析，建立可证明的压缩上限。
- 两阶段搜索：通道剪枝+零样本精度代理+结构变异，高效找最优压缩方案。
- 首次实现3D点云模型无源码压缩，适合部署在Jetson等边缘设备。
将3D点云模型部署到NVIDIA Jetson Orin Nano等边缘硬件时，受限于计算与内存预算。现有压缩方法需访问原始源码，无法用于厂商和模型库普遍分发的ONNX二进制文件。本文提出H3DNAS——一个硬件感知的模型压缩框架，可直接在ONNX计算图上操作，无需原始源码、架构定义或梯度信息。其三大贡献为：(1) 提出通道依赖图（CDG），将ONNX算子分为四类约束，并证明自由参数比例ρ_f具有拓扑不变性，可在O(|V|+|E|)时间内计算出可证明的压缩上限；(2) 设计两阶段层级搜索：通过L1重要性通道选择剪枝候选，以输出保真度作为零样本无标签代理排序，对帕累托最优候选应用GhostConv结构变异；(3) 实现首个无需源码的3D点云模型压缩流水线，完全通过ONNX图手术完成，无需原始架构定义。在ModelNet40上，H3DNAS使PointNet、PointNet++、PointMLP的参数量分别减少65.5%、43.2%、49.1%，推理速度提升1.99×、1.29×、1.67×，精度损失极小。代码已公开。
原文摘要 · Abstract (English)
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 three contributions: (1) a \textbf{Channel Dependency Graph (CDG)} that classifies ONNX operators into four constraint classes and formally establishes that the free parameter fraction $\rho_f$ is topological invariant, a provable compression ceiling computable in $\mathcal{O}(|V|+|E|)$; (2) a \textbf{Two-Stage Hierarchical Search} that prunes candidate architectures by $L_1$-importance channel selection, ranks them by output fidelity as a zero-shot label-free proxy, and applies GhostConv structural mutation to Pareto-optimal candidates; and (3) the \textbf{first source-code-free compression pipeline for 3D point cloud models}, operating entirely via ONNX graph surgery with no original architecture definition required. On ModelNet40, H3DNAS reduces the number of parameters in PointNet, PointNet++, and PointMLP by $65.5\%$, $43.2\%$, and $49.1\%$, respectively, while achieving $1.99\times$, $1.29\times$, and $1.67\times$ inference speedups with negligible loss in accuracy. The source code is publicly available\footnote{https://github.com/ClarityLab-Org/h3dnas}.
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