Developing complex, real world graphics applications which leverage multiple GPUs and computers for interactive 3D rendering tasks is a complex task. It requires expertise in distributed systems and parallel rendering in addition to the application domain itself. We present a mature parallel rendering framework which provides a large set of features, algorithms and system integration for a wide range of real-world research and industry applications. Using the Equalizer parallel rendering framework, we show how a wide set of generic algorithms can be integrated in the framework to help application scalability and development in many different domains, highlighting how concrete applications benefit from the diverse aspects and use cases of Equalizer. We present novel parallel rendering algorithms, powerful abstractions for large visualization setups and virtual reality, as well as new experimental results for parallel rendering and data distribution.
Furthermore, various applications and frameworks have used Equalizer for new research in visualization. On the application side, RTT Deltagen , Bino , Livre and RTNeuron @cite_23 are the most mature examples and are presented in sApplications . On the framework side, Omegalib @cite_25 , a framework used in the Cave2, made significant progress in integrating 2D collaborative workspaces like Sage 2 with 3D immersive content. Lambers et.al. developed a framework for visualizing remote sensing data @cite_38 on large displays and immersive installations.
@cite_38 @cite_25 @cite_23
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In the domain of large-scale visualization instruments, hybrid reality environments (HREs) are a recent innovation that combines the best-in-class capabilities of immersive environments, with the best-in-class capabilities of ultra-high-resolution display walls. HREs create a seamless 2D 3D environment that supports both information-rich analysis as well as virtual reality simulation exploration at a resolution matching human visual acuity. Co-located research groups in HREs tend to work on a variety of tasks during a research session (sometimes in parallel), and these tasks require 2D data views, 3D views, linking between them and the ability to bring in (or hide) data quickly as needed. In this paper we present Omegalib, a software framework that facilitates application development on HREs. Omegalib is designed to support dynamic reconfigurability of the display environment, so that areas of the display can be interactively allocated to 2D or 3D workspaces as needed. Compared to existing frameworks and toolkits, Omegalib makes it possible to have multiple immersive applications running on a cluster-controlled display system, have different input sources dynamically routed to applications, and have rendering results optionally redirected to a distributed compositing manager. Omegalib supports pluggable front-ends, to simplify the integration of third-party libraries like OpenGL, OpenSceneGraph, and the Visualization Toolkit (VTK). We present examples of applications developed with Omegalib for the 74-megapixel, 72-tile CAVE2™ system, and show how a Hybrid Reality Environment proved effective in supporting work for a co-located research group in the environmental sciences. In this paper, we address the problem of semantic scene segmentation of RGB-D images of indoor scenes. We propose a novel image region labeling method which augments CRF formulation with hard mutual exclusion (mutex) constraints. This way our approach can make use of rich and accurate 3D geometric structure coming from Kinect in a principled manner. The final labeling result must satisfy all mutex constraints, which allows us to eliminate configurations that violate common sense physics laws like placing a floor above a night stand. Three classes of mutex constraints are proposed: global object co-occurrence constraint, relative height relationship constraint, and local support relationship constraint. We evaluate our approach on the NYU-Depth V2 dataset, which consists of 1449 cluttered indoor scenes, and also test generalization of our model trained on NYU-Depth V2 dataset directly on a recent SUN3D dataset without any new training. The experimental results show that we significantly outperform the state-of-the-art methods in scene labeling on both datasets. We describe a concurrent visualization pipeline designed for operation in a production supercomputing environment. The facility was initially developed on the NASA Ames "Columbia" supercomputer for a massively parallel forecast model (GEOS4). During the 2005 Atlantic hurricane season, GEOS4 was run 4 times a day under tight time constraints so that its output could be included in an ensemble prediction that was made available to forecasters at the National Hurricane Center. Given this time-critical context, we designed a configurable concurrent pipeline to visualize multiple global fields without significantly affecting the runtime model performance or reliability. We use MPEG compression of the accruing images to facilitate live low-bandwidth distribution of multiple visualization streams to remote sites. We also describe the use of our concurrent visualization framework with a global ocean circulation model, which provides a 864-fold increase in the temporal resolution of practically achievable animations. In both the atmospheric and oceanic circulation models, the application scientists gained new insights into their model dynamics, due to the high temporal resolution animations attainable Many robotics and Augmented Reality (AR) systems that use sparse keypoint-based visual maps operate in large and highly repetitive environments, where pose tracking and localization are challenging tasks. Additionally, these systems usually face further challenges, such as limited computational power, or insufficient memory for storing large maps of the entire environment. Thus, developing compact map representations and improving retrieval is of considerable interest for enabling large-scale visual place recognition and loop-closure. In this paper, we propose a novel approach to compress descriptors while increasing their discriminability and match-ability, based on recent advances in neural networks. At the same time, we target resource-constrained robotics applications in our design choices. The main contributions of this work are twofold. First, we propose a linear projection from descriptor space to a lower-dimensional Euclidean space, based on a novel supervised learning strategy employing a triplet loss. Second, we show the importance of including contextual appearance information to the visual feature in order to improve matching under strong viewpoint, illumination and scene changes. Through detailed experiments on three challenging datasets, we demonstrate significant gains in performance over state-of-the-art methods.