A framework for digital sunken relief generation based on 3D geometric models

Sunken relief is a special art form of sculpture whereby the depicted shapes are sunk into a given surface. This is traditionally created by laboriously carving materials such as stone. Sunken reliefs often utilize the engraved lines or strokes to strengthen the impressions of a 3D presence and to highlight the features which otherwise are unrevealed. In other types of reliefs, smooth surfaces and their shadows convey such information in a coherent manner. Existing methods for relief generation are focused on forming a smooth surface with a shallow depth which provides the presence of 3D figures. Such methods unfortunately do not help the art form of sunken reliefs as they omit the presence of feature lines. We propose a framework to produce sunken reliefs from a known 3D geometry, which transforms the 3D objects into three layers of input to incorporate the contour lines seamlessly with the smooth surfaces. The three input layers take the advantages of the geometric information and the visual cues to assist the relief generation. This framework alters existing techniques in line drawings and relief generation, and then combines them organically for this particular purpose

Digital relief generation from 3D models

It is difficult to extend image-based relief generation to high-relief generation, as the images contain insufficient height information. To generate reliefs from three-dimensional (3D) models, it is necessary to extract the height fields from the model, but this can only generate bas-reliefs. To overcome this problem, an efficient method is proposed to generate bas-reliefs and high-reliefs directly from 3D meshes. To produce relief features that are visually appropriate, the 3D meshes are first scaled. 3D unsharp masking is used to enhance the visual features in the 3D mesh, and average smoothing and Laplacian smoothing are implemented to achieve better smoothing results. A nonlinear variable scaling scheme is then employed to generate the final bas-reliefs and high-reliefs. Using the proposed method, relief models can be generated from arbitrary viewing positions with different gestures and combinations of multiple 3D models. The generated relief models can be printed by 3D printers. The proposed method provides a means of generating both high-reliefs and bas-reliefs in an efficient and effective way under the appropriate scaling factors

Analysis of Bas-Relief Generation Techniques

Simplifying the process of generating relief sculptures has been an interesting topic of research in the past decade. A relief is a type of sculpture that does not entirely extend into three-dimensional space. Instead, it has details that are carved into a flat surface, like wood or stone, such that there are slight elevations from the flat plane that define the subject of the sculpture. When viewed orthogonally straight on, a relief can look like a full sculpture or statue in the respect that a full sense of depth from the subject can be perceived. Creating such a model manually is a tedious and difficult process, akin to the challenges a painter may face when designing a convincing painting. Like with painting, certain digital tools (3D modeling programs most commonly) can make the process a little easier, but can still take a lot of time to obtain sufficient details. To further simplify the process of relief generation, a sizable amount of research has gone into developing semi-automated processes of creating reliefs based on different types of models. These methods can vary in many ways, including the type of input used, the computational time required, and the quality of the resulting model. The performance typically depends on the type of operations applied to the input model, and usually user-specified parameters to modify its appearance. In this thesis, we try to accomplish a few related topics. First, we analyze previous work in the field and briefly summarize the procedures to emphasize a variety of ways to solve the problem. We then look at specific algorithms for generating reliefs from 2D and 3D models. After explaining two of each type, a “basic” approach, and a more sophisticated one, we compare the algorithms based on their difficulty to implement, the quality of the results, and the time to process. The final section will include some more sample results of the previous algorithms, and will suggest possible ideas to enhance their results, which could be applied in continuing research on the topic

Computer Assisted Relief Generation - a Survey

In this paper we present an overview of the achievements accomplished to date in the field of computer aided relief generation. We delineate the problem, classify the different solutions, analyze similarities, investigate the evelopment and review the approaches according to their particular relative strengths and weaknesses. In consequence this survey is likewise addressed to researchers and artists through providing valuable insights into the theory behind the different concepts in this field and augmenting the options available among the methods presented with regard to practical application

3D digital relief generation.

This thesis investigates a framework for generating reliefs. Relief is a special kind of sculptured artwork consisting of shapes carved on a surface so as to stand out from the surrounding background. Traditional relief creation is done by hand and is therefore a laborious process. In addition, hand-made reliefs are hard to modify. Contrasted with this, digital relief can offer more flexibility as well as a less laborious alternative and can be easily adjusted. This thesis reviews existing work and offers a framework to tackle the problem of generating three types of reliefs: bas reliefs, high reliefs and sunken reliefs. Considerably enhanced by incorporating gradient operations, an efficient bas relief generation method has been proposed, based on 2D images. An improvement of bas relief and high relief generation method based on 3D models has been provided as well, that employs mesh representation to process the model. This thesis is innovative in describing and evaluating sunken relief generation techniques. Two types of sunken reliefs have been generated: one is created with pure engraved lines, and the other is generated with smooth height transition between lines. The latter one is more complex to implement, and includes three elements: a line drawing image provides a input for contour lines; a rendered Lambertian image shares the same light direction of the relief and sets the visual cues and a depth image conveys the height information. These three elements have been combined to generate final sunken reliefs. It is the first time in computer graphics that a method for digital sunken relief generation has been proposed. The main contribution of this thesis is to have proposed a systematic framework to generate all three types of reliefs. Results of this work can potentially provide references for craftsman, and this work could be beneficial for relief creation in the fields of both entertainment and manufacturing

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

State of the Art on Stylized Fabrication

© 2018 The Authors Computer Graphics Forum © 2018 The Eurographics Association and John Wiley & Sons Ltd. Digital fabrication devices are powerful tools for creating tangible reproductions of 3D digital models. Most available printing technologies aim at producing an accurate copy of a tridimensional shape. However, fabrication technologies can also be used to create a stylistic representation of a digital shape. We refer to this class of methods as ‘stylized fabrication methods’. These methods abstract geometric and physical features of a given shape to create an unconventional representation, to produce an optical illusion or to devise a particular interaction with the fabricated model. In this state-of-the-art report, we classify and overview this broad and emerging class of approaches and also propose possible directions for future research

3D Face Reconstruction from Light Field Images: A Model-free Approach

Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest. However, it is still an ill-posed problem and most methods rely on prior models hence undermining the accuracy of the recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI) obtained from light field cameras and learn CNN models that recover horizontal and vertical 3D facial curves from the respective horizontal and vertical EPIs. Our 3D face reconstruction network (FaceLFnet) comprises a densely connected architecture to learn accurate 3D facial curves from low resolution EPIs. To train the proposed FaceLFnets from scratch, we synthesize photo-realistic light field images from 3D facial scans. The curve by curve 3D face estimation approach allows the networks to learn from only 14K images of 80 identities, which still comprises over 11 Million EPIs/curves. The estimated facial curves are merged into a single pointcloud to which a surface is fitted to get the final 3D face. Our method is model-free, requires only a few training samples to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single light field images under varying poses, expressions and lighting conditions. Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces reconstruction errors by over 20% compared to recent state of the art