Architectural Visualization Volume 4 Full Training Cg Workshop
Vanina Mazzillo
As developers of high-end software for the analysis and visualization of industrial computed tomography (CT) data, we see it as our duty to show you how to use the growing range of functions of our software as efficiently as possible. Just sign up for one of our various training courses.
IMT 561 Visualization Design (4)
Students develop a human-centered visualization design practice using real-world data. This process includes applying graphic principles of visual encoding to data; conducting design explorations using sketches and prototyping; and gathering user feedback to assess output. Design workshops provide opportunities for hands-on engagement with concepts and technical skills.
View course details in MyPlan: IMT 561
In this work, we address the problem of jointly estimating albedo, normals, depth and 3D spatially-varying lighting from a single image. Most existing methods formulate the task as image-to-image translation, ignoring the 3D properties of the scene. However, indoor scenes contain complex 3D light transport where a 2D representation is insufficient. In this paper, we propose a unified, learning-based inverse rendering framework that formulates 3D spatially-varying lighting. Inspired by classic volume rendering techniques, we propose a novel Volumetric Spherical Gaussian representation for lighting, which parameterizes the exitant radiance of the 3D scene surfaces on a voxel grid. We design a physics-based differentiable renderer that utilizes our 3D lighting representation, and formulates the energy-conserving image formation process that enables joint training of all intrinsic properties with the re-rendering constraint. Our model ensures physically correct predictions and avoids the need for ground-truth HDR lighting which is not easily accessible. Experiments show that our method outperforms prior works both quantitatively and qualitatively, and is capable of producing photorealistic results for AR applications such as virtual object insertion even for highly specular objects.
3D volumes of neurons. Convolutional Neural Networks take advantage of the fact that the input consists of images and they constrain the architecture in a more sensible way. In particular, unlike a regular Neural Network, the layers of a ConvNet have neurons arranged in 3 dimensions: width, height, depth. (Note that the word depth here refers to the third dimension of an activation volume, not to the depth of a full Neural Network, which can refer to the total number of layers in a network.) For example, the input images in CIFAR-10 are an input volume of activations, and the volume has dimensions 32x32x3 (width, height, depth respectively). As we will soon see, the neurons in a layer will only be connected to a small region of the layer before it, instead of all of the neurons in a fully-connected manner. Moreover, the final output layer would for CIFAR-10 have dimensions 1x1x10, because by the end of the ConvNet architecture we will reduce the full image into a single vector of class scores, arranged along the depth dimension. Here is a visualization:
Convolution Demo. Below is a running demo of a CONV layer. Since 3D volumes are hard to visualize, all the volumes (the input volume (in blue), the weight volumes (in red), the output volume (in green)) are visualized with each depth slice stacked in rows. The input volume is of size \(W_1 = 5, H_1 = 5, D_1 = 3\), and the CONV layer parameters are \(K = 2, F = 3, S = 2, P = 1\). That is, we have two filters of size \(3 \times 3\), and they are applied with a stride of 2. Therefore, the output volume size has spatial size (5 - 3 + 2)/2 + 1 = 3. Moreover, notice that a padding of \(P = 1\) is applied to the input volume, making the outer border of the input volume zero. The visualization below iterates over the output activations (green), and shows that each element is computed by elementwise multiplying the highlighted input (blue) with the filter (red), summing it up, and then offsetting the result by the bias.
The objective is an understanding of the procedure for converting a series of 2D images into 3D images, movies, and fly-throughs. This is the basis of the medical CT scan, industrial non-destructive testing, and much of scientific visualization. The second objective is introduction to two software packages---ImageJ and VisIt---that are both extremely powerful, will run on your laptop, and are free. When finished with this course, you should be able to perform visualizations of 3D data sets when provided with data in open access formats such as raw binary, stacked TIFF, and HDF5. You will run Mathematica notebooks in class as we discuss: (a) 2D image sequences into 3D volumes, (b) histograms and binarization, (c) image transformations such as Gaussian filtering, distance transform, and watershed transform, (d) connected component analysis, and (e) simple movie making.
This class explains the principles of volume rendering and the art of constructing the right transfer functions. It explores the drawbacks and extravagant possibilities of this new visualization modality in applications involving real-world data. The class is for those who have an interest in learning about volumetric rendering in the Wolfram Language. Basic knowledge of the Wolfram Language and 3D rendering and processing are recommended.
In addition to modeling and rendering volumetric phenomena, volume rendering is essential to scientific and engineering applications that require visualization of three-dimensional data sets. Examples include visualization of data acquired by medical imaging devices or resulting from computational fluid dynamics simulations. Users of interactive volume rendering applications rely on the performance of modern graphics accelerators for efficient data exploration and feature discovery.
In essence, the role of the optical model is to describe how particles in the volume interact with light. For example, the most commonly used model assumes that the volume consists of particles that simultaneously emit and absorb light. More complex models incorporate local illumination and volumetric shadows, and they account for light scattering effects. Optical parameters are specified by the data values directly, or they are computed from applying one or more transfer functions to the data. The goal of the transfer function in visualization applications is to emphasize or classify features of interest in the data. Typically, transfer functions are implemented by texture lookup tables, though simple functions can also be computed in the fragment shader. For example, Figure 39-2 illustrates the use of a transfer function to extract material boundaries from a CT scan of a tooth.
Volume rendering is an important graphics and visualization technique. A volume renderer can be used for displaying not only surfaces of a model but also the intricate detail contained within. The first half of this chapter presented a typical implementation of a texture-based volume renderer with view-aligned proxy geometry. In Section 39.5, two advanced techniques built upon the basic implementation were described. The presented techniques improve the quality of images by adding volumetric shadows, translucency effects, and random detail to the standard rendering model.
In this course, Milan shares his industry expertise to hone your architectural illustration skills. Dive into 3ds Max and learn how to use its digital tools to shape, model, render, and retouch striking visualizations of the buildings you envision.
Milan Stevanović is an architect and 3D artist based in Niš, Serbia. He is the founder of A+ Studio, where their main focus is architecture and architectural visualization, but are also established in the field of graphic design. Their main goal is providing their clients with a unique design and the feeling that comes with it.
SlicerMorph streamlines digital morphology research by enabling effortless data import, visualization, measurement, annotation, and geometric morphometric analysis on 3D data, including volumetric scans (CTs and MRs) and 3D surface scans, all within the 3D Slicer application. Say goodbye to multiple programs, different file formats, and workflows!
Shared-memory multiprocessor workstations have become widely available to the visualization community. Direct volume rendering of unstructured grids is a computationally intensive problem that is of substantial interest in scientific visualization. This paper presents a parallel algorithm for a voxelization-based direct volume rendering of unstructured grids and its implementation on a shared-memory multiprocessor.
*This course is required for those students so designated by the Admissions Committee. Typically, this course will be required for students who do not have significant pre-architectural training. This five-week course begins mid-July and concludes mid-August.
The GraphicsPro.g4dn bundles are designed for high-end graphics workloads, such as media production, rendering, data science, architectural, and seismic visualization applications. Additionally, they are well suited for compute workloads including intelligent video analytics (IVA), small-scale ML model training, and ML inference. A graphicsPro.g4dn bundle offers 16vCPUs, 64 GB of RAM, 16 GB of video memory, 225 GB of temporary NVMe SSD local instance store, and a minimum 100 GB of persistent storage for the user volume and root volumes. GraphicsPro.g4dn bundles provide a Windows 10 virtual workstation experience and Ubuntu desktop experience. See EC2 G4dn instances for more details about G4dn instances and NVIDIA T4 GPUs.
Many 3D graphics systems use texture mapping to apply images, or textures, to geometric objects. Commodity PC graphics cards are fast at texturing and can efficiently render slices of a 3D volume, with real time interaction capabilities. Workstation GPUs are even faster, and are the basis for much of the production volume visualization used in medical imaging, oil and gas, and other markets (2007). In earlier years, dedicated 3D texture mapping systems were used on graphics systems such as Silicon Graphics InfiniteReality, HP Visualize FX graphics accelerator, and others. This technique was first described by Bill Hibbard and Dave Santek.[9]
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