Abstract Real-time holographic communication enables immersive remote interaction by transmitting dynamic three-dimensional representations of users and environments, providing a stronger sense of presence than conventional video conferencing. However, the high data throughput requirements of volumetric streaming introduce challenges related to bandwidth variability and heterogeneous device capabilities. This paper presents an end-to-end system for scalable, real-time holographic telepresence, designed to operate within a fixed volumetric data transmission budget. The proposed approach combines importance-driven and semantic-aware filtering to preserve perceptually and interaction-critical details, while degrading less relevant data. We also leverage gesture recognition to automatically identify and emphasize points of interest, enhancing interactivity and realism. Experimental results demonstrate high visual fidelity and responsive performance with low bandwidth requirements, enabling efficient holographic communication for diverse use cases across different platforms.

Abstract Oriented bounding box (OBB) hierarchies can be used instead of hierarchies based on axis‐aligned bounding boxes (AABB), providing tighter fitting to the underlying geometric structures and resulting in improved interference tests, such as ray‐geometry intersections. In this paper, we present a method for the fast, parallel transformation of an existing bounding volume hierarchy (BVH), based on AABBs, into a hierarchy based on oriented bounding boxes. To this end, we parallelise a high‐quality OBB extraction algorithm from the literature to operate as a standalone OBB estimator and further extend it to efficiently build an OBB hierarchy in a bottom up manner. This agglomerative approach allows for fast parallel execution and the formation of arbitrary, high‐quality OBBs in bounding volume hierarchies. The method is fully implemented on the GPU and extensively evaluated with ray intersections.

Abstract Spatial data structures, such as k-d trees and bounding volume hierarchies, are extensively used in computer graphics for the acceleration of spatial queries in ray tracing, nearest neighbour searches and other tasks. Typically, the splitting strategy employed during the construction of such structures is based on the greedy evaluation of a predefined objective function, resulting in a less than optimal subdivision scheme. In this work, for the first time, we propose the use of unsupervised deep learning to infer the structure of a fixed-depth k-d tree from a constant, subsampled set of the input primitives, based on the recursive evaluation of the cost function at hand. This results in high-quality upper spatial hierarchy, inferred in constant time and without paying the intractable price of a fully recursive tree optimisation. The resulting fixed-depth tree can then be further expanded, in parallel, into either a full k-d tree or transformed into a bounding volume hierarchy, with any known conventional tree builder. The approach is generic enough to accommodate different cost functions, such as the popular surface area and volume heuristics. We experimentally validate that the resulting hierarchies have competitive traversal performance with respect to established tree builders, while maintaining minimal overhead in construction times.

Opening design is a major consideration in architectural buildings during early structural layout specification. Decisions regarding the geometric characteristics of windows, skylights, hatches, etc., greatly impact the overall energy efficiency, airflow and appearance of a building, both internally and externally. In this work, we employ a goal-based, illumination-driven approach to opening design using a Bayesian Optimization approach, based on Gaussian Processes. A method is proposed that allows a designer to easily set lighting intentions along with qualitative and quantitative characteristics of desired openings. All parameters are optimized within a cost minimization framework to calculate geometrically feasible, architecturally admissible and aesthetically pleasing openings of any desired shape, while respecting the designer's lighting constraints.

Low noise, high resolution, fast and accurate T₁ maps from MRI images of the brain can be performed using a dual flip angle method. However, B₁ field inhomogeneity, which is particularly problematic at high field strengths (e.g., 3T), limits the ability of the scanner to deliver the prescribed flip angle, introducing errors into the T₁ maps that limit the accuracy of quantitative analyses based on those maps. A dual repetition time method was used for acquiring a B₁ map to correct that inhomogeneity. Additional inaccuracies due to misregistration of the acquired T₁-weighted images were corrected by rigid registration, and the effects of misalignment on the T₁ maps were compared to those of B₁ inhomogeneity in 19 normal subjects. However, since B₁ map acquisition takes up precious scanning time and most retrospective studies do not have B₁ map, we designed a template-based correction strategy. B₁ maps from different subjects were aligned using a twelve-parameter affine registration. Recomputed T₁ maps showed an important improvement with respect to the noncorrected maps: histograms of all corrected maps exhibited two peaks corresponding to white and gray matter tissues, while unimodal histograms were observed in all uncorrected maps because of the inhomogeneity. A method to detect the best nonsubject-specific B₁ correction based on a set of features was designed. The optimum set of weighting factors for those features was computed. The best available B₁ correction was detected in almost all subjects while corrections comparable to the T₁ map corrected using the B₁ map from the same subject were detected in the others.