基于几何关系预测齿轮组运动，无需预设关节。
DYNAMO: Dependency-Aware Deep Learning Framework for Articulated Assembly Motion Prediction
- 通过依赖感知神经网络从点云直接预测部件运动轨迹。
- 在693个合成齿轮装配体上实现高精度、时序一致的运动预测。
- 适合机械设计自动化与3D感知领域研究者使用。
从静态几何理解可动机械装配体的运动仍是3D感知与设计自动化中的核心挑战。以往针对门、笔记本等日常可动物体的方法通常假设简化运动结构或依赖关节标注，但在齿轮类机械装配中，运动由几何耦合（如齿啮合或轴对齐）产生，现有方法难以仅凭几何推断关联运动。为此，我们构建了MechBench基准数据集，包含693个多样化的合成齿轮装配体，其部件运动轨迹为逐部件真值，运动由接触与传动驱动，而非预定义关节。在此基础上，提出DYNAMO——一种依赖感知神经模型，直接从分割后的CAD点云预测各部件的SE(3)运动轨迹。实验表明，DYNAMO优于多个强基线，在多种齿轮配置下均实现准确且时序一致的预测。MechBench与DYNAMO共同建立了一个数据驱动学习CAD装配体耦合运动的新系统框架。
原文摘要 · Abstract (English)
Understanding the motion of articulated mechanical assemblies from static geometry remains a core challenge in 3D perception and design automation. Prior work on everyday articulated objects such as doors and laptops typically assumes simplified kinematic structures or relies on joint annotations. However, in mechanical assemblies like gears, motion arises from geometric coupling, through meshing teeth or aligned axes, making it difficult for existing methods to reason about relational motion from geometry alone. To address this gap, we introduce MechBench, a benchmark dataset of 693 diverse synthetic gear assemblies with part-wise ground-truth motion trajectories. MechBench provides a structured setting to study coupled motion, where part dynamics are induced by contact and transmission rather than predefined joints. Building on this, we propose DYNAMO, a dependency-aware neural model that predicts per-part SE(3) motion trajectories directly from segmented CAD point clouds. Experiments show that DYNAMO outperforms strong baselines, achieving accurate and temporally consistent predictions across varied gear configurations. Together, MechBench and DYNAMO establish a novel systematic framework for data-driven learning of coupled mechanical motion in CAD assemblies.
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