用点云对齐与物理约束引导扩散模型生成更合理的可动物体。
Guiding Diffusion-Based Articulated Object Generation by Partial Point Cloud Alignment and Physical Plausibility Constraints
- 用SDF表示部件形状，通过点云对齐损失指导反向扩散过程。
- 加入不穿透和可动性约束，使生成物体更符合物理规律。
- 支持类别信息输入，提升点云对齐效果，适合机器人交互场景。
可动物体是日常环境中重要的可交互对象。本文提出PhysNAP，一种基于扩散模型的可动物体生成方法，通过部分点云对齐和物理合理性约束提升生成质量。模型使用有符号距离函数（SDF）表示部件形状，利用预测SDF计算点云对齐损失，引导反向扩散过程。同时，基于部件SDF施加非穿透和可动性约束，以生成更符合物理规律的结构。此外，模型具备类别感知能力，在提供类别信息时可进一步改善点云对齐。在PartNet-Mobility数据集上的评估表明，PhysNAP在约束一致性上优于无引导基线扩散模型，并实现了生成能力与物理合理性的权衡。
原文摘要 · Abstract (English)
Articulated objects are an important type of interactable objects in everyday environments. In this paper, we propose PhysNAP, a novel diffusion model-based approach for generating articulated objects that aligns them with partial point clouds and improves their physical plausibility. The model represents part shapes by signed distance functions (SDFs). We guide the reverse diffusion process using a point cloud alignment loss computed using the predicted SDFs. Additionally, we impose non-penetration and mobility constraints based on the part SDFs for guiding the model to generate more physically plausible objects. We also make our diffusion approach category-aware to further improve point cloud alignment if category information is available. We evaluate the generative ability and constraint consistency of samples generated with PhysNAP using the PartNet-Mobility dataset. We also compare it with an unguided baseline diffusion model and demonstrate that PhysNAP can improve constraint consistency and provides a tradeoff with generative ability.
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