Plane-Based Optimization for 3D Object Reconstruction from Single Line Drawings

Reconstructing 3D objects from single line drawings is often desirable in computer vision and graphics applications. If the line drawing of a complex 3D object is decomposed into primitives of simple shape, the object can be easily reconstructed. We propose an effective method to conduct the line drawing separation and turn a complex line drawing into parametric 3D models. This is achieved by recursively separating the line drawing using two types of split faces. Our experiments show that the proposed separation method can generate more basic and simple line drawings, and its combination with the example-based reconstruction can robustly recover wider range of complex parametric 3D objects than previous methods.

Progressive reconstruction of 3D objects from a single free-hand line drawing

Single 2D line drawing is a straightforward method to illustrate 3D objects. The faces of an object depicted by a line drawing give very useful information for the reconstruction of its 3D geometry. Two recently proposed methods for face identification from line drawings are based on two steps: finding a set of circuits that may be faces and searching for real faces from the set according to some criteria. The two steps, however, involve two combinatorial problems. The number of the circuits generated in the first step grows exponentially with the number of edges of a line drawing. These circuits are then used as the input to the second combinatorial search step. When dealing with objects having more faces, the combinatorial explosion prevents these methods from finding solutions within feasible time. This paper proposes a new method to tackle the face identification problem by a variable-length genetic algorithm with a novel heuristic and geometric constraints incorporated for local search. The hybrid GA solves the two combinatorial problems simultaneously. Experimental results show that our algorithm can find the faces of a line drawing having more than 30 faces much more efficiently. In addition, simulated annealing for solving the face identification problem is also implemented for comparison.

According to three-dimensional geometrical properties, some theorems and corollaries of face identification from line drawing with hidden lines are proposed in this paper. This paper improves the depth-first search algorithm by adding constraints. By this means, the cycles that are unreasonable are deleted directly and the efficiency of face identification is increased. The examples show that the face identification algorithm proposed in this paper not only can identify faces exactly from line drawing but increases the efficiency of the original algorithm.

The reconstruction of 3D objects from 2D line drawings is regarded as one of the key topics in the field of computer vision. The ongoing research is mainly focusing on the reconstruction of 3D objects that are mapped only from 2D straight lines, and that are symmetric in nature. Commonly, this approach only produces basic and simple shapes that are mostly flat or rather polygonized in nature, which is normally attributed to inability to handle curves. To overcome the above-mentioned limitations, a technique capable of handling non-symmetric drawings that encompass curves is considered. This paper discusses a novel technique that can be used to reconstruct 3D objects containing curved lines. In addition, it highlights an application that has been developed in accordance with the suggested technique that can convert a freehand sketch to a 3D shape using a mobile phone.

The faces in a 2D fine drawing of an object provide important information for the reconstruction of its 3D geometry. In this paper, a graph-based optimization method is proposed for identifying the faces is a line drawing. The face identification is formulated as a maximum weight clique problem. This formulation is proven to be equivalent to the formulation proposed by Shpitalni and Upson (1996). The advantage of our formulation is that it enables one to develop a much faster algorithm to find the faces in a drawing. The significant improvement in speed is derived from two algorithms provided: the depth-first graph search for quickly generating possible faces from a drawing; and the maximum weight clique finding for obtaining the optimal face configurations of the drawing. The experimental results shown that our algorithm generates the same results of face identification as Shpitalni and Lipson's method, but is much faster when dealing with objects of more than 20 faces.

Freehand sketch on a paper is commonly seen being used as it is an effective convenient way to express rough ideas. The sketch is typically transformed to be a 3D CAD model by a designer for subsequent operations such as rapid prototyping process where a physical prototype is fabricated from a 3D CAD model. To speed up the realization of an idea, a research called adaptive layered manufacturing from overtraced freehand sketch has been being developed as an attempt to shorten steps from having a sketch drawing to obtaining a physical prototype. Presented in this paper is the current progress of the research. A single line drawing identification module is being developed to transform a paper-based overtraced freehand sketch to be a single line drawing. This module is required to generate more information about the sketch (i.e., the number of lines and their starting points and endpoints) since the initial information is limited to a batch of points. The algorithms developed for this module have been implemented on LabVIEW program. Some examples of sketch to a single line drawing transformation are also presented in this paper.

Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding

This study compares different forms of compliance function, such as the linear, polynomial and Gaussian forms, in 3D reconstruction of polyhedral objects, under the framework of support vector machine for regression (SVR). The correlations among different regularities are considered in the proposed form. This study also provides a systematic method to calculate the corresponding coefficients of regularities based on the 2D line drawings and corresponding 3D objects library.

Sketch-based modeling studies reconstructing models from sketches automatically, allowing users visualize design concepts rapidly. Generating CAD models based on user sketches helps reduce the learning curve for novice users, which promotes the everyday use of CAD software, and expands its reach to non-professional groups. While various algorithms study automatically generating models from single sketch or line drawing, they often produce non-editable models or editable models limited to simple extrusion operations. To improve this issue, we propose a novel sketch-based modeling system, Sketch2Seq, which generates complex, semantic, and editable CAD models. Our system eliminates the need for additional annotations from users and produces models that support subsequent application in commercial software. The core of our method lies in understanding users' design intent from CAD sketches. We design a novel sketch segmentation network for identifying diverse operation features in CAD sketches, which utilizes geometric features of strokes and different levels of topological connections. Additionally, to tackle the segmentation task, a dataset for CAD sketch segmentation is introduced. Comparative experiments and ablation evaluations prove the effectiveness of the proposed method. Based on segmentation result, coarse CAD sequences are generated and progressively executed. Meanwhile, the orders and parameters of the CAD sequences are optimized with context models and input sketches. All algorithms are integrated into a user interface. Experiments and evaluations validate the feasibility and superiority of our entire system which is able to reconstruct more complex features and achieve better results for longer sequence.

Identification of faces in line drawings by edge decomposition

Spatial reasoning on multi-view line drawings by state-of-the-art supervised deep networks is recently shown with puzzling low performances on the SPARE3D dataset [14]. Based on the fact that self-supervised learning is helpful when a large number of data are available, we propose two self-supervised learning approaches to improve the baseline performance for view consistency reasoning and camera pose reasoning tasks on the SPARE3D dataset. For the first task, we use a self-supervised binary classification network to contrast the line drawing differences between various views of any two similar 3D objects, enabling the trained networks to effectively learn detail-sensitive yet view-invariant line drawing representations of 3D objects. For the second type of task, we propose a self-supervised multi-class classification framework to train a model to select the correct corresponding view from which a line drawing is rendered. Our method is even helpful for the downstream tasks with unseen camera poses. Experiments show that our method could significantly increase the baseline performance in SPARE3D, while some popular self-supervised learning methods cannot.

In this paper we assume that 3D objects are known as a database containing 3D co-ordinates and gray tone level of the object points. With the help of the database, we construct a sequence of planes containing a set of 2D objects. In order to reconstruct several 3D branching objects we solve the following problems: 1) divide the set of 2D objects to subsets of similar 2D objects; 2) find the branches of similar 2D objects; and 3) connect similar 2D objects and branches. We introduce the term morphological similarity to solve the first problem. We apply morphological sequences to find the branches. We determine the "corresponding" essential points to connect similar 2D objects. We performed experiments to validate the theory, using real data obtained from ore deposit.

Primal access recognition of visual objects (PARVO), a computer vision system that addresses the problem of fast and generic recognition of unexpected 3D objects from single 2D views, is considered. Recently, recognition by components (RBC), which is a new human image understanding theory, based on some psychological results, has been proposed as an explanation of how PARVO works. However, no systematic computational evaluation of its many aspects has yet been reported. The PARVO system discussed is a first step toward this goal, since its design respects and makes explicit the main assumptions of the proposed theory. It analyzes single-view 2D line drawings of 3D objects typical of the ones used in human image understanding studies. It is designed to handle partially occluded objects of different shape and dimension in various spatial orientations and locations in the image plane. The system is shown to successfully compute generic descriptions and then recognize many common man-made objects.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

We present an optical method for information watermarking of three-dimensional (3D) objects by digital holography. A hidden image is embedded by double phase encoding in a phase-shift digital hologram of the 3D object. We decode the watermarked hologram to reconstruct the hidden image and the 3D object. We use either the entire hologram or a part of it to decode the hidden image. Experiments are presented to illustrate the ability to recover both the 3D object and the decoded hidden image. Digital holograms of the 3D object are obtained by optical experiments. The watermarking process, 3D object reconstruction, and hidden image recovery are performed digitally. To the best of our knowledge, this is the first report of 3D object watermarking by use of a phase encoding technique and digital holography.