From on-line sketching to 2D and 3D geometry: A fuzzy knowledge based system

The paper describes the development of a fuzzy knowledge based prototype system for conceptual design. This real time system is designed to infer user’s sketching intentions, to segment sketched input and generate corresponding geometric primitives: straight lines, circles, arcs, ellipses, elliptical arcs, and B-spline curves. Topology information (connectivity, unitary constraints and pairwise constraints) is received dynamically from 2D sketched input and primitives. From the 2D topology information, a more accurate 2D geometry can be built up by applying a 2D geometric constraint solver. Subsequently, 3D geometry can be received feature by feature incrementally. Each feature can be recognised by inference knowledge in terms of matching its 2D primitive configurations and connection relationships. The system accepts not only sketched input, working as an automatic design tools, but also accepts user’s interactive input of both 2D primitives and special positional 3D primitives. This makes it easy and friendly to use. The system has been tested with a number of sketched inputs of 2D and 3D geometry

Sketching space

In this paper, we present a sketch modelling system which we call Stilton. The program resembles a desktop VRML browser, allowing a user to navigate a three-dimensional model in a perspective projection, or panoramic photographs, which the program maps onto the scene as a `floor' and `walls'. We place an imaginary two-dimensional drawing plane in front of the user, and any geometric information that user sketches onto this plane may be reconstructed to form solid objects through an optimization process. We show how the system can be used to reconstruct geometry from panoramic images, or to add new objects to an existing model. While panoramic imaging can greatly assist with some aspects of site familiarization and qualitative assessment of a site, without the addition of some foreground geometry they offer only limited utility in a design context. Therefore, we suggest that the system may be of use in `just-in-time' CAD recovery of complex environments, such as shop floors, or construction sites, by recovering objects through sketched overlays, where other methods such as automatic line-retrieval may be impossible. The result of using the system in this manner is the `sketching of space' - sketching out a volume around the user - and once the geometry has been recovered, the designer is free to quickly sketch design ideas into the newly constructed context, or analyze the space around them. Although end-user trials have not, as yet, been undertaken we believe that this implementation may afford a user-interface that is both accessible and robust, and that the rapid growth of pen-computing devices will further stimulate activity in this area

Solid reconstruction using recognition of quadric surfaces from orthographic views

International audienceThe reconstruction of 3D objects from 2D orthographic views is crucial for maintaining and further developing existing product designs. A B-rep oriented method for reconstructing curved objects from three orthographic views is presented by employing a hybrid wire-frame in place of an intermediate wire-frame. The Link-Relation Graph (LRG) is introduced as a multi-graph representation of orthographic views, and quadric surface features (QSFs) are defined by special basic patterns of LRG as well as aggregation rules. By hint-based pattern matching in the LRGs of three orthographic views in an order of priority, the corresponding QSFs are recognized, and the geometry and topology of quadric surfaces are recovered simultaneously. This method can handle objects with interacting quadric surfaces and avoids the combinatorial search for tracing all the quadric surfaces in an intermediate wire-frame by the existing methods. Several examples are provided

Subspace procrustes analysis

Postprint (author's final draft

3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense point cloud representing a 3D reconstruction of the input sketch(es). The point cloud is then converted into a polygon mesh. At the heart of our method lies a deep, encoder-decoder network. The encoder converts the sketch into a compact representation encoding shape information. The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints. The multi-view maps are then consolidated into a 3D point cloud by solving an optimization problem that fuses depth and normals across all viewpoints. Based on our experiments, compared to other methods, such as volumetric networks, our architecture offers several advantages, including more faithful reconstruction, higher output surface resolution, better preservation of topology and shape structure.Comment: 3DV 2017 (oral

SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes

The objective of this paper is 3D shape understanding from single and multiple images. To this end, we introduce a new deep-learning architecture and loss function, SilNet, that can handle multiple views in an order-agnostic manner. The architecture is fully convolutional, and for training we use a proxy task of silhouette prediction, rather than directly learning a mapping from 2D images to 3D shape as has been the target in most recent work. We demonstrate that with the SilNet architecture there is generalisation over the number of views -- for example, SilNet trained on 2 views can be used with 3 or 4 views at test-time; and performance improves with more views. We introduce two new synthetics datasets: a blobby object dataset useful for pre-training, and a challenging and realistic sculpture dataset; and demonstrate on these datasets that SilNet has indeed learnt 3D shape. Finally, we show that SilNet exceeds the state of the art on the ShapeNet benchmark dataset, and use SilNet to generate novel views of the sculpture dataset.Comment: BMVC 2017; Best Poste

3D reconstruction of point clouds using multi-view orthographic projections

Cataloged from PDF version of article.A method to reconstruct 3D point clouds using multi-view orthographic projections is examined. Point clouds are generated by means of a stochastic process. This stochastic process is designed to generate point clouds that mimic microcalcification formation in breast tissue. Point clouds are generated using a Gibbs sampler algorithm. Orthographic projections of point clouds from any desired orientation are generated. Volumetric intersection method is employed to perform the reconstruction from these orthographic projections. The reconstruction may yield erroneous reconstructed points. The types of these erroneous points are analyzed along with their causes and a performance measure based on linear combination is devised. Experiments have been designed to investigate the effect of the number of projections and the number of points to the performance of reconstruction. Increasing the number of projections and decreasing the number of points resulted in better reconstructions that are more similar to the original point clouds. However, it is observed that reconstructions do not improve considerably upon increasing the number of projections after some number. This method of reconstruction serves well to find locations of original points.Topçu, OsmanM.S

Inferring Implicit 3D Representations from Human Figures on Pictorial Maps

In this work, we present an automated workflow to bring human figures, one of the most frequently appearing entities on pictorial maps, to the third dimension. Our workflow is based on training data and neural networks for single-view 3D reconstruction of real humans from photos. We first let a network consisting of fully connected layers estimate the depth coordinate of 2D pose points. The gained 3D pose points are inputted together with 2D masks of body parts into a deep implicit surface network to infer 3D signed distance fields (SDFs). By assembling all body parts, we derive 2D depth images and body part masks of the whole figure for different views, which are fed into a fully convolutional network to predict UV images. These UV images and the texture for the given perspective are inserted into a generative network to inpaint the textures for the other views. The textures are enhanced by a cartoonization network and facial details are resynthesized by an autoencoder. Finally, the generated textures are assigned to the inferred body parts in a ray marcher. We test our workflow with 12 pictorial human figures after having validated several network configurations. The created 3D models look generally promising, especially when considering the challenges of silhouette-based 3D recovery and real-time rendering of the implicit SDFs. Further improvement is needed to reduce gaps between the body parts and to add pictorial details to the textures. Overall, the constructed figures may be used for animation and storytelling in digital 3D maps.Comment: to be published in 'Cartography and Geographic Information Science