Reliable manipulation of previously unseen objects remains a fundamental challenge for autonomous robotic systems operating in unstructured environments.In particular, robust pick-and-place planning directly from noisy and only partial real-world observations, where object surfaces are inherently incomplete due to occlusions (e.g., bottom faces on a tabletop), is difficult. As a result, many existing methods rely on strong object priors (e.g., CAD models) or to assume placement on continuous, flat support surfaces such as planar tabletops, without explicitly accounting for edge proximity or inclined supports.In this work, we introduce a robust probabilistic placeability metric that evaluates 6D~object placement poses from partial observations by jointly scoring object stability, graspability, and clearance from raw point cloud geometry.Using this metric, we generate diverse multi-orientation placement candidates and condition grasp scoring on these placements, enabling model-free unified pick-and-place reasoning.Simulation and real-robot experiments on unseen objects and challenging support geometries confirm that our metric yields accurate stability predictions and consistently improves end-to-end pick-and-place success by producing stable, collision-free grasp–place pairs directly from partial point clouds.