Deep Points Consolidation

In this paper, we present a consolidation method that is based on a new representation of 3D point sets. The key idea is to augment each surface point into a deep point by associating it with an inner point that resides on the meso-skeleton, which consists of a mixture of skeletal curves and sheets. The deep points representation is a result of a joint optimization applied to both ends of the deep points. The optimization objective is to fairly distribute the end points across the surface and the meso-skeleton, such that the deep point orientations agree with the surface normals. The optimization converges where the inner points form a coherent meso-skeleton, and the surface points are consolidated with the missing regions completed. The strength of this new representation stems from the fact that it is comprised of both local and non-local geometric information. We demonstrate the advantages of the deep points consolidation technique by employing it to consolidate and complete noisy point-sampled geometry with large missing parts.

Turning Agents into Psychological Principals: Aligning Interests of Non-Owners Through Psychological Ownership

Principals who delegate tasks to agents face the perennial challenge of overcoming agency problems. We investigate whether feelings of ownership among senior managers in the absence of formal ownership can align agents' interests with those of principals, thus turning agents into psychological principals. Using a moderated mediation model, we find that psychological ownership is positively related to company performance through the mediating effect of individual-level entrepreneurial behaviour. We also find that the effect of psychological ownership on individual-level entrepreneurial behaviour and, ultimately, company performance is weaker for high levels of monitoring compared to low levels. These findings offer important contributions to agency, psychological ownership, and entrepreneurship literatures.