text: We present and evaluate an approach for human-in-the-loop specification of shape reconstruction with annotations for basic robot-object interactions. Our method is based on the idea of model annotation: the addition of simple cues to an underlying object model to specify shape and delineate a simple task. The goal is to explore reducing the complexity of CAD-like interfaces so that novice users can quickly recover an object's shape and describe a manipulation task that is then carried out by a robot. The object modeling and interaction annotation capabilities are tested with a user study and compared against results obtained using existing approaches. The approach has been analyzed using a variety of shape comparison, grasping, and manipulation metrics, and tested with the PR2 robot platform, where it was shown to be successful.
  @cite_121: We propose a sweep-based approach to the freeform deformation of three-dimensional objects. Instead of using a volume enclosing the whole object, we approximate only its deformable parts using sweep surfaces. The vertices on the object boundary are bound to the sweep surfaces and follow their deformation. Several sweep surfaces can be organized into a hierarchy so that they interact with each other in a controlled manner. Thus we can support intuitively plausible shape deformation of objects of arbitrary topology with multiple control handles. A sweep-based approach also provides important advantages such as volume preservation. We demonstrate the effectiveness of our technique in several examples.
  @cite_108: Modeling 3D objects from sketches is a process that requires several challenging problems including segmentation, recognition and reconstruction. Some of these tasks are harder for humans and some are harder for the machine. At the core of the problem lies the need for semantic understanding of the shape’s geometry from the sketch. In this paper we propose a method to model 3D objects from sketches by utilizing humans specifically for semantic tasks that are very simple for humans and extremely difficult for the machine, while utilizing the machine for tasks that are harder for humans. The user assists recognition and segmentation by choosing and placing specific geometric primitives on the relevant parts of the sketch. The machine first snaps the primitive to the sketch by fitting its projection to the sketch lines, and then improves the model globally by inferring geosemantic constraints that link the different parts. The fitting occurs in real-time, allowing the user to be only as precise as needed to have a good starting configuration for this non-convex optimization problem. We evaluate the accessibility of our approach with a user study.
summary: In the work of @cite_121 , a is extracted from 3D object meshes and later used to deform them by performing simple modifications of the underlying proxy. This approach highlights the power of simplified representations for object analysis and modification. The work of @cite_108 shows the use of these simple sweep-based proxies to fit 2D sketches and transform them to 3D objects.