Representation Of Volumetric Data Research Articles

Differentiable Design Galleries: A Differentiable Approach to Explore the Design Space of Transfer Functions.

The transfer function is crucial for direct volume rendering (DVR) to create an informative visual representation of volumetric data. However, manually adjusting the transfer function to achieve the desired DVR result can be time-consuming and unintuitive. In this paper, we propose Differentiable Design Galleries, an image-based transfer function design approach to help users explore the design space of transfer functions by taking advantage of the recent advances in deep learning and differentiable rendering. Specifically, we leverage neural rendering to learn a latent design space, which is a continuous manifold representing various types of implicit transfer functions. We further provide a set of interactive tools to support intuitive query, navigation, and modification to obtain the target design, which is represented as a neural-rendered design exemplar. The explicit transfer function can be reconstructed from the target design with a differentiable direct volume renderer. Experimental results on real volumetric data demonstrate the effectiveness of our method.

2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data

In medical-data driven learning, 3D convolutional neural networks (CNNs) have started to show superior performance to 2D CNNs in numerous deep learning tasks, proving the added value of 3D spatial information in feature representation. However, the difficulty in collecting more training samples to converge, more computational resources and longer execution time make this approach less applied. Also, applying transfer learning on 3D CNN is challenging due to a lack of publicly available pre-trained 3D models. To tackle these issues, we proposed a novel 2D strategical representation of volumetric data, namely 2.75D. In this work, the spatial information of 3D images is captured in a single 2D view by a spiral-spinning technique. As a result, 2D CNN networks can also be used to learn volumetric information. Besides, we can fully leverage pre-trained 2D CNNs for downstream vision problems. We also explore a multi-view 2.75D strategy, 2.75D 3 channels (2.75D × 3), to boost the advantage of 2.75D. We evaluated the proposed methods on three public datasets with different modalities or organs (Lung CT, Breast MRI, and Prostate MRI), against their 2D, 2.5D, and 3D counterparts in classification tasks. Results show that the proposed methods significantly outperform other counterparts when all methods were trained from scratch on the lung dataset. Such performance gain is more pronounced with transfer learning or in the case of limited training data. Our methods also achieved comparable performance on other datasets. In addition, our methods achieved a substantial reduction in time consumption of training and inference compared with the 2.5D or 3D method.

Differential Transform for Video-Based Plenoptic Point Cloud Coding.

Point cloud compression has been studied in standard bodies and we are here concerned with the Moving Picture Experts Group video-based point cloud compression (V-PCC) solution. Plenoptic point clouds (PPC) is a novel volumetric data representation wherein points are associated with colors in all viewing directions to improve realism. It is sampled as a number ( Nc ) of attribute colors per point. We propose a new method for the efficient video-based compression of PPC that is backwards compatible with the existing single-color V-PCC decoder. V-PCC generates three image atlases which are encoded using an image/video encoder. We assume there may be a reference color which is to be encoded as the main payload. We generate Nc+3 atlases and we produce Nc differential images against the reference color image. Those difference images are pixel-wise transformed using an Nc -point discrete cosine transform, generating Nc transformed atlases which are encoded, forming the secondary payload. Such secondary information is the plenoptic enhancement to the point cloud. If there is no reference attribute, we skip the differences and use the lowest frequency of the transformed atlases as the main payload. Results are presented that show an unrivaled performance of the proposed method.

SEMANTIC SEGMENTATION OF INDOOR POINT CLOUDS USING CONVOLUTIONAL NEURAL NETWORK

Abstract. As Building Information Modelling (BIM) thrives, geometry becomes no longer sufficient; an ever increasing variety of semantic information is needed to express an indoor model adequately. On the other hand, for the existing buildings, automatically generating semantically enriched BIM from point cloud data is in its infancy. The previous research to enhance the semantic content rely on frameworks in which some specific rules and/or features that are hand coded by specialists. These methods immanently lack generalization and easily break in different circumstances. On this account, a generalized framework is urgently needed to automatically and accurately generate semantic information. Therefore we propose to employ deep learning techniques for the semantic segmentation of point clouds into meaningful parts. More specifically, we build a volumetric data representation in order to efficiently generate the high number of training samples needed to initiate a convolutional neural network architecture. The feedforward propagation is used in such a way to perform the classification in voxel level for achieving semantic segmentation. The method is tested both for a mobile laser scanner point cloud, and a larger scale synthetically generated data. We also demonstrate a case study, in which our method can be effectively used to leverage the extraction of planar surfaces in challenging cluttered indoor environments.

Progressive Direct Volume-to-Volume Transformation.

We present a novel technique to generate transformations between arbitrary volumes, providing both expressive distances and smooth interpolates. In contrast to conventional morphing or warping approaches, our technique requires no user guidance, intermediate representations (like extracted features), or blending, and imposes no restrictions regarding shape or structure. Our technique operates directly on the volumetric data representation, and while linear programming approaches could solve the underlying problem optimally, their polynomial complexity makes them infeasible for high-resolution volumes. We therefore propose a progressive refinement approach designed for parallel execution that is able to quickly deliver approximate results that are iteratively improved toward the optimum. On this basis, we further present a new approach for the streaming selection of time steps in temporal data that allows for the reconstruction of the full sequence with a user-specified error bound. We finally demonstrate the utility of our technique for different applications, compare our approach against alternatives, and evaluate its characteristics with a variety of different data sets.

Volumetric Data Reduction in a Compressed Sensing Framework

AbstractIn this paper, we investigate compressed sensing principles to devise an in‐situ data reduction framework for visualization of volumetric datasets. We exploit the universality of the compressed sensing framework and show that the proposed method offers a refinable data reduction approach for volumetric datasets. The accurate reconstruction is obtained from partial Fourier measurements of the original data that are sensed without any prior knowledge of specific feature domains for the data. Our experiments demonstrate the superiority of surfacelets for efficient representation of volumetric data. Moreover, we establish that the accuracy of reconstruction can further improve once a more effective basis for a sparser representation of the data becomes available.

An ArcScene plug-in for volumetric data conversion, modeling and spatial analysis

Representation of volumetric data is necessary in hydrology, meteorology, ecology, environmental science and so on for modeling and visualization. As popular GIS software, ArcScene supports MultiPatch format for modeling and spatial analysis of volumetric data. It also supports conversion of 3D file formats into MultiPatch. But there are obvious limitations: (1) ArcScene does not support conversion of popular 3D formats such as Wavefront Objects (OBJ) and Visualization ToolKit (VTK) into MultiPatch; (2) after the conversion, the entire dataset only forms one patch which is inconvenient for further spatial analysis; (3) ArcScene does not support 3D contouring. In this study, we have successfully developed an ArcScene plug-in for converting OBJ and VTK file formats into MultiPatch, for accessing and editing MultiPatch files, and for tracing 3D contours based on MultiPatch. The ArcScene plug-in is characteristic of a time complexity of O(N), strong data structure in terms of spatial analysis, user-friendly operation, high robustness and good extendibility, and therefore it enriches ArcScene functionalities and users can benefit from the new plug-in for various applications.

Density and isothermal compressibility of ionic liquids from perturbed hard-sphere chain equation of state

This paper addresses the modeling of the density and isothermal compressibility of some ionic liquids (ILs) using Yukawa hard-sphere chain (YHSC) equation of sate (EOS) plus first-order perturbation theory. Three pure-component parameters that appeared in the EOS have been optimized for the representation of volumetric data. These parameters reflect the segment number, non-bonded segment–segment interaction energy, and the segment size. This work considered the chains that interact through a range-parameter of Yukawa potential with 1.8. The reliability of the proposed model has been assessed by comparing the results with 3153 experimental data points over a broad range of pressures and temperatures for which, their measured values were available in the literature. Our calculations on the density of studied ILs reproduced very accurately the experimental PVT data. The overall average absolute deviation (AAD) of the calculated densities from literature data was found to be 0.54%. We have also assessed the proposed perturbed hard-sphere chain equation of state (PHSC EOS) in the prediction of isothermal compressibility (κT) by taking 1043 literature data points; AAD was found to be 1.63%. Furthermore, the linear dependency of the liquid bulk modules (BT) on pressure has been successfully investigated via the proposed model.

VOTS: VOlume doTS as a Point‐Based Representation of Volumetric Data

Abstract We present Volume dots (Vots), a new primitive for volumetric data modelling, processing, and rendering. Vots are a point‐based representation of volumetric data. An individual Vot is specified by the coefficients of a Taylor series expansion, i.e. the function value and higher order derivatives at a specific point. A Vot does not only represent a single sample point, it represents the underlying function within a region. With the Vots representation we have a more intuitive and high‐level description of the volume data. This allows direct analytical examination and manipulation of volumetric datasets. Vots enable the representation of the underlying scalar function with specified precision. User‐centric importance sampling is also possible, i.e., unimportant volume parts are still present but represented with just very few Vots. As proof of concept, we show Maximum Intensity Projection based on Vots. Categories and Subject Descriptors (according to ACM CCS): I.3.6 [Computer Graphics]: Graphics Data Structures and Data Types

Virtual Bronchoscopy

Patients potentially suffer and are exposed to danger during invasive bronchoscopic diagnosis and surgery. In order to reduce this hazardous risk, we have developed an interactive virtual environment for the simulation of bronchoscopy (in short, called "virtual bronchoscopy"). Because of this state-of-the-art application, medical doctors can now obtain pre-operative information and perform pilot examinations in a virtual environment without any invasive or needless surgery. This 3D lung volume data of the patient is first acquired from CT and/or MRI scanning, without any pain being inflicted upon the patient. Then a vessel-tracking process is used to extract the patient's bronchial tree from the data. It is important to note that while manual tracking is tedious and labor-intensive, fully automatic tracking may not be as reliable in such a critical medical application. Thus a semi-automatic tracking technique called the Intelligent Path Tracker, which provides automation and sufficient user control during the tracking process, is most useful. This methodology is applied to a virtual bronchoscopy session, where doctors can use a 3D pen input device to navigate and visualize the bronchial tree of patients in a natural and interactive manner. To support an interactive frame rate, we also propose a new volume rendering acceleration technique, named IsoRegion Leaping. Through this technique visualization is further accelerated using a distributed rendering process based upon a TCP/IP network of low-cost PCs. Combining these approaches enables a 256x256x256 volumetric data representation of a human lung to be navigated and visualized at a frame rate of over 10 Hz in our virtual bronchoscopy system.

Volume models for volumetric data

Given a set of points on the boundary of an object derived from volumetric data, how can one represent the object and, in particular visualize it from these points? This problem is addressed by our research on the representation of points at the boundary of an object as a union of simple boundary primitives. We use volumetric data in the customary sense, but an additional feature for our purpose is the availability of an inside-outside test for any point within the volume. Our problem is, therefore, a restricted form of the general problem of visualizing an arbitrary cloud of points. Representing and visualizing can be vague concepts. As an intuitive example of the kind of representation we are looking for, assume we have data somehow representing a human head. In the first approximation, the head can be represented by a sphere. The surface area and the volume of the sphere give us rough, but useful, estimates of the corresponding properties for the head. At the same time, the position and radius of the sphere give us an idea of the translation and scaling to apply to get the head in some canonical position. If, instead, we fit an ellipsoid, the additional degrees of freedom might let us obtain the parameters of the rotations to apply. Of course, we cannot independently obtain estimates for the scaling, volume, or area. The obtainable estimates depend on the context. Whereas human perception deals very well with these ambiguities, computer visualization tends to fall short. The new representation of volumetric data based on union of spheres shows promise in achieving stability.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>