This paper presents a novel method to localize and segment objects on close-range table-top scenarios acquired with a depth sensor. The method is based on a novel objectness measure that evaluates how likely a 3D region in space (defined by an oriented 3D bounding box) could contain an object. Within a parametrized volume of interest placed above the table plane, a set of 3D bounding boxes is generated that exhaustively covers the parameter space. Efficiently evaluating \u2014 thanks to integral volumes and parallel computing\u2014 the 3D objectness at each sampled bounding box allows defining a set of regions in space with high probability of containing an object. Bounding boxes characterized by high objectness are then processed by means of a global optimization stage aimed at discarding inconsistent object hypotheses with respect to the scene. We evaluate the effectiveness of the method for the task of scene segmentation
