Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data resulting from analysis of such data or simulations. Visualisation is often the first step in making sense of the data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualisations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR AR) headsets and associated controllers are becoming invaluable tools. In this work we introduce scenery, a flexible VR AR visualisation framework for the Java VM that can handle mesh and arbitrarily large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features and detail example applications, such as its use in the biomedical image analysis software Fiji, or for visualising agent-based simulations.
A particularly popular in scientific visualisation is @cite_18 : VTK offers rendering of both geometric and volumetric data, using an OpenGL 2.1 renderer. However, VTK's complexity has also grown over the years and its API is becoming more complex, making it difficult to change internals without breaking existing applications ( @math ). A more recent development is , which brings VTK to web browsers. @cite_10 is a visualisation toolkit tailored to high-speed, volumetric microscopy and supports multi-channel multi-timepoint data, but focuses solely on volumetric data and does not support VR AR. Another special-purpose framework is @cite_17 , which focuses on efficient rendering of a large number of discrete particles, and provides a thin abstraction layer over the used graphics API for the developer. @cite_30 does general-purpose image visualisation tasks, and supports multi-timepoint data, but no out-of-core volume rendering, or VR AR.
@cite_30 @cite_18 @cite_10 @cite_17
2089086072 2166103081 2001749563 2338968644
The Visualization Toolkit (VTK) is an open source, permissively licensed, cross-platform toolkit for scientific data processing, visualization, and data analysis. It is over two decades old, originally developed for a very different graphics card architecture. Modern graphics cards feature fully programmable, highly parallelized architectures with large core counts. VTK’s rendering code was rewritten to take advantage of modern graphics cards, maintaining most of the toolkit’s programming interfaces. This offers the opportunity to compare the performance of old and new rendering code on the same systems cards. Significant improvements in rendering speeds and memory footprints mean that scientific data can be visualized in greater detail than ever before. The widespread use of VTK means that these improvements will reap significant benefits. We propose a new approach to efficiently render large volumetric data sets. The system achieves interactive to real-time rendering performance for several billion voxels. Our solution is based on an adaptive data representation depending on the current view and occlusion information, coupled to an efficient ray-casting rendering algorithm. One key element of our method is to guide data production and streaming directly based on information extracted during rendering. Our data structure exploits the fact that in CG scenes, details are often concentrated on the interface between free space and clusters of density and shows that volumetric models might become a valuable alternative as a rendering primitive for real-time applications. In this spirit, we allow a quality performance trade-off and exploit temporal coherence. We also introduce a mipmapping-like process that allows for an increased display rate and better quality through high quality filtering. To further enrich the data set, we create additional details through a variety of procedural methods. We demonstrate our approach in several scenarios, like the exploration of a 3D scan (81923 resolution), of hypertextured meshes (163843 virtual resolution), or of a fractal (theoretically infinite resolution). All examples are rendered on current generation hardware at 20--90 fps and respect the limited GPU memory budget. This paper presents the first volume visualization system that scales to petascale volumes imaged as a continuous stream of high-resolution electron microscopy images. Our architecture scales to dense, anisotropic petascale volumes because it: (1) decouples construction of the 3D multi-resolution representation required for visualization from data acquisition, and (2) decouples sample access time during ray-casting from the size of the multi-resolution hierarchy. Our system is designed around a scalable multi-resolution virtual memory architecture that handles missing data naturally, does not pre-compute any 3D multi-resolution representation such as an octree, and can accept a constant stream of 2D image tiles from the microscopes. A novelty of our system design is that it is visualization-driven: we restrict most computations to the visible volume data. Leveraging the virtual memory architecture, missing data are detected during volume ray-casting as cache misses, which are propagated backwards for on-demand out-of-core processing. 3D blocks of volume data are only constructed from 2D microscope image tiles when they have actually been accessed during ray-casting. We extensively evaluate our system design choices with respect to scalability and performance, compare to previous best-of-breed systems, and illustrate the effectiveness of our system for real microscopy data from neuroscience. The seminal multiple-view stereo benchmark evaluations from Middlebury and by have played a major role in propelling the development of multi-view stereopsis (MVS) methodology. The somewhat small size and variability of these data sets, however, limit their scope and the conclusions that can be derived from them. To facilitate further development within MVS, we here present a new and varied data set consisting of 80 scenes, seen from 49 or 64 accurate camera positions. This is accompanied by accurate structured light scans for reference and evaluation. In addition all images are taken under seven different lighting conditions. As a benchmark and to validate the use of our data set for obtaining reasonable and statistically significant findings about MVS, we have applied the three state-of-the-art MVS algorithms by , , and to the data set. To do this we have extended the evaluation protocol from the Middlebury evaluation, necessitated by the more complex geometry of some of our scenes. The data set and accompanying evaluation framework are made freely available online. Based on this evaluation, we are able to observe several characteristics of state-of-the-art MVS, e.g. that there is a tradeoff between the quality of the reconstructed 3D points (accuracy) and how much of an object's surface is captured (completeness). Also, several issues that we hypothesized would challenge MVS, such as specularities and changing lighting conditions did not pose serious problems. Our study finds that the two most pressing issues for MVS are lack of texture and meshing (forming 3D points into closed triangulated surfaces).