We describe an automated scene modeling system that consists of two components operating in an interleaved fashion: an incremental modeler that builds solid models from range imagery; and a sensor planner that analyzes the resulting model and computes the next sensor position. This planning component is target-driven and computes sensor positions using model information about the imaged surfaces and the unexplored space in a scene. The method is shape-independent and uses a continuous-space representation that preserves the accuracy of sensed data. It is able to completely acquire a scene by repeatedly planning sensor positions, utilizing a partial model to determine volumes of visibility for contiguous areas of unexplored scene. These visibility volumes are combined with sensor placement constraints to compute sets of occlusion-free sensor positions that are guaranteed to improve the quality of the model. We show results for the acquisition of a scene that includes multiple, distinct objects with high occlusion.

In this article we present a method for automatically constructing a solid (in the CAD sense) model of an unknown object from range images. The model acquisition system provides facilities for range image acquisition, solid model construction and model merging: a solid representation is derived from a mesh surface that models the range data from each view, which is then merged with the model built from previous sensing operations. This modeling system has the benefit of constructing a solid model at each phase of the acquisition process, and is able to model parts that are difficult or impossible using other methods, such as extremely thin parts or those with deep through-holes. We introduce a technique that utilizes the incomplete model resulting from the merging process to plan the next sensing operation by finding a sensor viewpoint that will improve the fidelity of the model. A number of examples are given for the modeling of various objects that include planar and curved surfaces, features such as through-holes, and large self-occlusions.

We present an incremental system that builds accurate CAD models of objects from multiple range images. Using a hybrid of surface mesh and volumetric representations, the system creates a "water-tight" 3D model at each step of the modeling process, allowing reasonable models to be built from a small number of views. We also present a method that can be used to plan the next view and reduce the number of scans needed to recover the object. Results are presented for the creation of 3D models of a computer game controller, a hip joint prosthesis, and a mechanical strut.

This paper describes a robotic system that builds a 3D CAD model of an object incrementally from multiple range images. It motivates the generation of a solid model at each stage of the modeling process, allowing the use of well-defined geometric algorithms to perform the merging and integration task. The data from each imaging operation is represented by a mesh, which is then extruded in the viewing direction to form a solid model. These solids are merged as they are acquired into a composite model of the object. We describe an algorithm that builds a solid model from a mesh surface and present experimental results of reconstructing a complex object. In addition, we discuss an approach to completely automating the model acquisition process by integration with previous sensor-planning results.

This paper discusses an approach to automating CAD model acquisition by creating binary space partitioning (BSP) trees from laser range data. Distinct views of the object, each represented by a BSP tree, are then merged using set operations. BSP trees have proven their utility in 3D modeling, graphics and image processing, and their tree structure allows efficient algorithms to be developed that are compact and numerically robust. These properties are of primary importance when considering an intermediate representation between raw sensor data and existing CAD models.