Abstract:We describe a method of constructing a B-rep solid model from a single hidden-line removed sketch view of a 3D object. The main steps of our approach are as follows. The sketch is first tidied in 2D (to remove digitisation errors). Line Iabelling is used to deduce the initial topology of the object and to locate hidden faces. Constraints are then produced from the line labelling and features in the drawing (such as probable symmetry) involving the unknown face coefficients and point depths. A least squares sol… Show more
“…Its main drawback is that it becomes a direct regularity only after faces have been detected in the line drawing, and this is a complex task when hidden edges are drawn (see [46], [47], [48] and [49]). The alternative of sketching only frontal geometry (without hidden edges) and inferring hidden parts later, when 3D geometry has been given a preliminary interpretation, was explored by [10] and [16].…”
Section: Categorisation Of Regularitiesmentioning
confidence: 99%
“…Only a few reconstruction algorithms are able to act as engines in this modelling by sketch approach aimed at aiding conceptual design by single-view sketches, because they must be tolerant to faults in the drawing and work incrementally [10]. Optimisation approaches have been successfully used in this context [11] [12] because models can be updated whenever the input sketch is modified and the reconstruction process is tolerant to imperfections.…”
Section: Introductionmentioning
confidence: 99%
“…Mathematical formulation of perceptual cues (also called "regularities", or "artefacts") has been studied by a small group of researchers within the reconstruction community ( [10], [14], [15] and [16]) and has led to a poorly defined objective function, thus making a global minimum very difficult to find.…”
An engine that automatically reconstructs a large variety of polyhedral, origami and wire-frame objects from single-view sketched drawings generated in a calligraphic interface is presented. The engine has two stages. An innovative optimisation-based line-drawing beautifier stage is introduced to convert rough sketches into tidied-up line drawings. Optimisation-based 3D reconstruction follows. Solutions are provided with which to overcome the problems associated with earlier approaches to optimisationbased 3D reconstruction. Suitable adjustments in the optimisation algorithms are proposed; simple and efficient tentative models are introduced, and current regularities are categorised in order to allow the objective function to be simplified. All three actions help to prevent local optima and improve the computational efficiency of optimisation-based 3D reconstruction. They all proved to be effective techniques to reduce the typical failure rate of optimisation approaches. A discussion of results that validate the engine is also provided.4
“…Its main drawback is that it becomes a direct regularity only after faces have been detected in the line drawing, and this is a complex task when hidden edges are drawn (see [46], [47], [48] and [49]). The alternative of sketching only frontal geometry (without hidden edges) and inferring hidden parts later, when 3D geometry has been given a preliminary interpretation, was explored by [10] and [16].…”
Section: Categorisation Of Regularitiesmentioning
confidence: 99%
“…Only a few reconstruction algorithms are able to act as engines in this modelling by sketch approach aimed at aiding conceptual design by single-view sketches, because they must be tolerant to faults in the drawing and work incrementally [10]. Optimisation approaches have been successfully used in this context [11] [12] because models can be updated whenever the input sketch is modified and the reconstruction process is tolerant to imperfections.…”
Section: Introductionmentioning
confidence: 99%
“…Mathematical formulation of perceptual cues (also called "regularities", or "artefacts") has been studied by a small group of researchers within the reconstruction community ( [10], [14], [15] and [16]) and has led to a poorly defined objective function, thus making a global minimum very difficult to find.…”
An engine that automatically reconstructs a large variety of polyhedral, origami and wire-frame objects from single-view sketched drawings generated in a calligraphic interface is presented. The engine has two stages. An innovative optimisation-based line-drawing beautifier stage is introduced to convert rough sketches into tidied-up line drawings. Optimisation-based 3D reconstruction follows. Solutions are provided with which to overcome the problems associated with earlier approaches to optimisationbased 3D reconstruction. Suitable adjustments in the optimisation algorithms are proposed; simple and efficient tentative models are introduced, and current regularities are categorised in order to allow the objective function to be simplified. All three actions help to prevent local optima and improve the computational efficiency of optimisation-based 3D reconstruction. They all proved to be effective techniques to reduce the typical failure rate of optimisation approaches. A discussion of results that validate the engine is also provided.4
“…Many methods have been proposed for automatically reconstructing 3D objects from single line drawings [5,7,9,10,11,15,17,19,20]. Among these methods, the two in [11] and [20] can handle more complex objects than the others.…”
“…The basic problem with this paper is that it is not even an advance on the work of Grimstead [2] [3], work which I pointed out to the authors when reviewing an earlier 2 draft of this paper submitted elsewhere. Indeed, Cao et al make the same extremely limiting assumptions.…”
Abstract-I comment on a paper describing a method for deducing the hidden topology of an object portrayed in a 2D natural line drawing. The principal problem with this paper is that it cannot be considered an advance on (or even an equal of) the state of the art, as the approach it describes makes the same limiting assumptions as approaches proposed ten years ago. There are also important omissions in the review of related work.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.