3D Visualization Techniques for Analysis and Archaeological Interpretation of GPR Data

The non-invasive detection and digital documentation of buried archaeological heritage by means of geophysical prospection is increasingly gaining importance in modern field archaeology and archaeological heritage management. It frequently provides the detailed information required for heritage protection or targeted further archaeological research. High-resolution magnetometry and ground-penetrating radar (GPR) became invaluable tools for the efficient and comprehensive non-invasive exploration of complete archaeological sites and archaeological landscapes. The analysis and detailed archaeological interpretation of the resulting large 2D and 3D datasets, and related data from aerial archaeology or airborne remote sensing, etc., is a time-consuming and complex process, which requires the integration of all data at hand, respective three-dimensional imagination, and a broad understanding of the archaeological problem; therefore, informative 3D visualizations supporting the exploration of complex 3D datasets and supporting the interpretative process are in great demand. This paper presents a novel integrated 3D GPR interpretation approach, centered around the flexible 3D visualization of heterogeneous data, which supports conjoint visualization of scenes composed of GPR volumes, 2D prospection imagery, and 3D interpretative models. We found that the flexible visual combination of the original 3D GPR datasets and images derived from the data applying post-processing techniques inspired by medical image analysis and seismic data processing contribute to the perceptibility of archaeologically relevant features and their respective context within a stratified volume. Moreover, such visualizations support the interpreting archaeologists in their development of a deeper understanding of the complex datasets as a starting point for and throughout the implemented interactive interpretative process

3D Visualization Techniques for Analysis and Archaeological Interpretation of GPR Data

The non-invasive detection and digital documentation of buried archaeological heritage by means of geophysical prospection is increasingly gaining importance in modern field archaeology and archaeological heritage management. It frequently provides the detailed information required for heritage protection or targeted further archaeological research. High-resolution magnetometry and ground-penetrating radar (GPR) became invaluable tools for the efficient and comprehensive non-invasive exploration of complete archaeological sites and archaeological landscapes. The analysis and detailed archaeological interpretation of the resulting large 2D and 3D datasets, and related data from aerial archaeology or airborne remote sensing, etc., is a time-consuming and complex process, which requires the integration of all data at hand, respective three-dimensional imagination, and a broad understanding of the archaeological problem; therefore, informative 3D visualizations supporting the exploration of complex 3D datasets and supporting the interpretative process are in great demand. This paper presents a novel integrated 3D GPR interpretation approach, centered around the flexible 3D visualization of heterogeneous data, which supports conjoint visualization of scenes composed of GPR volumes, 2D prospection imagery, and 3D interpretative models. We found that the flexible visual combination of the original 3D GPR datasets and images derived from the data applying post-processing techniques inspired by medical image analysis and seismic data processing contribute to the perceptibility of archaeologically relevant features and their respective context within a stratified volume. Moreover, such visualizations support the interpreting archaeologists in their development of a deeper understanding of the complex datasets as a starting point for and throughout the implemented interactive interpretative process

Reconstruction and Representation of Tubular Structures using Simplex Meshes

Modelling and reconstruction of tubular objects is a known problem in computer graphics. For computer aided surgical planning the constructed geometrical models need to be consistent and compact at the same time, which known approaches cannot guarantee. In this paper we present a new method for generating compact, topologically consistent, 2-manifold surfaces of branching tubular objects using a two-stage approach. The proposed method is based on connection of polygonal cross-sections along the medial axis and subsequent re nement. Higher order furcations can be handled correctly

Constructing Smooth Non-Manifold Meshes of Multi-Labeled Volumetric Datasets

This paper presents a method for constructing consistent non-manifold meshes of multi-labeled volu- metric datasets. This approach is di erent to traditional surface reconstruction algorithms which often only support extracting 2-manifold surfaces based on a binary voxel classi cation. However, in some { especially medical { applications, multi-labeled datasets, where up to eight di erently labeled voxels can be adjacent, are subject to visualization resulting in non-manifold meshes. In addition to an e cient surface reconstruction method, a constrained geometric lter is developed which can be applied to these non-manifold meshes without producing ridges at mesh junctions

User-centric transfer function specification in augmented reality

The quality of a 3D volume visualization heavily depends on a representative transfer function which is responsible for mapping the original density values to color and opacity. Finding a suitable transfer function is often a tedious task if done manually in a trial-and-error fashion by specifying piecewise linear functions for each color and opacity channel. Contrary, image-based models exploring features like gradient magnitude, histogram, or edge detection do not consider much user interaction as performed almost autonomously. Hence, we propose a new paradigm which integrates the user into the transfer function specification process. This allows an intuitive specification within an Augmented Reality environment by providing different predefined shape functions which can easily be adjusted. Moreover, a new approach is introduced which utilizes spatial information as an additional transfer function. This opens a completely new way of exploration in volume visualization