In this article we present Zahir, a framework for experimentation in Computer Graphics that provides a group of object-oriented base components that take care of common tasks in rendering techniques and algorithms, specially those of Non Photo-realistic Rendering (NPR). These components allow developers to implement rendering techniques and algorithms over static and animated meshes. Currently, Zahir is being used in a Master's Thesis and as support material in the undergraduate Computer Graphics course in University of Chile.
* Non Photo-realistic Rendering Applications. The inspiration for the initial version of Zahir was the software @cite_2 (RTSC). RTSC renders line drawings from 3D surfaces in a beautiful and optimal way. However, because it's a demonstration program, it's limited to static white objects (the models don't load any material or texture information). Those limitations inspired us to start working on a more flexible framework.
@cite_2
1492811546 2072438795 2011792403 2032837934
This paper introduces a programmable approach to non-photorealistic line drawing from 3D models, inspired by programmable shaders in traditional rendering. We propose a new image creation model where all operations are controlled through user-defined procedures. A view map describing all relevant support lines in the drawing and their topological arrangement is first created from the 3D model; a number of style modules operate on this map, by procedurally selecting, chaining or splitting lines, before creating strokes and assigning drawing attributes. The resulting drawing system permits flexible control of all elements of drawing style: first, different style modules can be applied to different types of lines in a view; second, the topology and geometry of strokes are entirely controlled from the programmable modules; and third, stroke attributes are assigned procedurally and can be correlated at will with various scene or view properties. Finally, we propose new density control strategies where strokes can be adapted or omitted to avoid visual clutter. We illustrate the components of our system and show how style modules successfully capture stylized visual characteristics that can be applied across a wide range of models. This article introduces a programmable approach to nonphotorealistic line drawings from 3D models, inspired by programmable shaders in traditional rendering. This approach relies on the assumption generally made in NPR that style attributes (color, thickness, etc.) are chosen depending on generic properties of the scene such as line characteristics or depth discontinuities, etc. We propose a new image creation model where all operations are controlled through user-defined procedures in which the relations between style attributes and scene properties are specified. A view map describing all relevant support lines in the drawing and their topological arrangement is first created from the 3D model so as to ensure the continuity of all scene properties along its edges; a number of style modules operate on this map, by procedurally selecting, chaining, or splitting lines, before creating strokes and assigning drawing attributes. Consistent access to properties of the scene is provided from the different elements of the map that are manipulated throughout the whole process. The resulting drawing system permits flexible control of all elements of drawing style: First, different style modules can be applied to different types of lines in a view; second, the topology and geometry of strokes are entirely controlled from the programmable modules; and third, stroke attributes are assigned procedurally and can be correlated at will with various scene or view properties. We illustrate the components of our system and show how style modules successfully encode stylized visual characteristics that can be applied across a wide range of models. We present a novel approach for detecting objects and estimating their 3D pose in single images of cluttered scenes. Objects are given in terms of 3D models without accompanying texture cues. A deformable parts-based model is trained on clusters of silhouettes of similar poses and produces hypotheses about possible object locations at test time. Objects are simultaneously segmented and verified inside each hypothesis bounding region by selecting the set of superpixels whose collective shape matches the model silhouette. A final iteration on the 6-DOF object pose minimizes the distance between the selected image contours and the actual projection of the 3D model. We demonstrate successful grasps using our detection and pose estimate with a PR2 robot. Extensive evaluation with a novel ground truth dataset shows the considerable benefit of using shape-driven cues for detecting objects in heavily cluttered scenes. This paper presents a non-photorealistic rendering technique that automatically generates a line drawing from a photograph. We aim at extracting a set of coherent, smooth, and stylistic lines that effectively capture and convey important shapes in the image. We first develop a novel method for constructing a smooth direction field that preserves the flow of the salient image features. We then introduce the notion of flow-guided anisotropic filtering for detecting highly coherent lines while suppressing noise. Our method is simple and easy to implement. A variety of experimental results are presented to show the effectiveness of our method in producing self-contained, high-quality line illustrations.