To reliably pick and place unknown objects under real-world sensing noise remains a challenging task, as existing methods rely on strong object priors (e.g., CAD models), or planar-support assumptions, limiting generalization and unified reasoning between grasping and placing. In this work, we introduce a generalized placeability metric that evaluates placement poses directly from noisy point clouds, without any shape priors. The metric jointly scores stability, graspability, and clearance. From raw geometry, we extract the support surfaces of the object to generate diverse candidates for multi-orientation placement and sample contacts that satisfy collision and stability constraints. By conditioning grasp scores on each candidate placement, our proposed method enables model-free unified pick-and-place reasoning and selects grasp-place pairs that lead to stable, collision-free placements. On unseen real objects and non-planar object supports, our metric delivers CAD-comparable accuracy in predicting stability loss and generally produces more physically plausible placements than learning-based predictors.

翻译：在现实世界感知噪声下可靠地拾取和放置未知物体仍然是一项具有挑战性的任务，因为现有方法依赖于强物体先验（如CAD模型）或平面支撑假设，限制了抓取与放置之间的泛化能力和统一推理。本工作提出了一种广义可放置性度量，可直接根据含噪声点云评估放置位姿，无需任何形状先验。该度量同时评估稳定性、可抓取性与间隙容限。我们从原始几何中提取物体的支撑表面，以生成多朝向放置的多样化候选位姿，并采样满足碰撞与稳定性约束的接触点。通过将抓取评分条件化于每个候选放置位姿，我们提出的方法实现了无模型统一拾放推理，并选择能产生稳定、无碰撞放置的抓取-放置组合。在未见过的真实物体与非平面物体支撑场景中，本度量在预测稳定性损失方面达到与CAD模型相当的精度，且通常比基于学习的预测器产生更具物理合理性的放置结果。