Abstract The explosion of data in astronomy has resulted in an era of unprecedented opportunities for discovery. The GAIA mission's catalog, containing a large number of light sources (mostly stars) with several parameters such as sky position and proper motion, is playing a significant role in advancing astronomy research and has been crucial in various scientific breakthroughs over the past decade. In its current release, more than 200 million stars contain a calibrated continuous spectrum, which is essential for characterizing astronomical information such as effective temperature and surface gravity, and enabling complex tasks like interstellar extinction detection and narrow‐band filtering. Even though numerous studies have been conducted to visualize and analyze the data in the SciVis and AstroVis communities, no work has attempted to leverage spectral information for visualization in real‐time. Interactive exploration of such complex, massive data presents several challenges for visualization. This paper introduces a novel multi‐resolution, spectrum‐preserving data structure and a progressive, real‐time visualization algorithm to handle the sheer volume of the data efficiently, enabling interactive visualization and exploration of the whole catalog's spectra. We show the efficiency of our method with our open‐source, interactive, web‐based tool for exploring the GAIA catalog, and discuss astronomically relevant use cases of our system.

Abstract We present a novel approach for rendering volumetric data including the Doppler effect of light. Similar to the acoustic Doppler effect, which is caused by relative motion between a sound emitter and an observer, light waves also experience compression or expansion when emitter and observer exhibit relative motion. We account for this by employing spectral volume rendering in an emission–absorption model, with the volumetric matter moving according to an accompanying vector field, and emitting and attenuating light at wavelengths subject to the Doppler effect. By introducing a novel piecewise linearear representation of the involved light spectra, we achieve accurate volume rendering at interactive frame rates. We compare our technique to rendering with traditional point‐based spectral representation, and demonstrate its utility using a simulation of galaxy formation.

Large-scale scientific data, such as weather and climate simulations, often comprise a large number of attributes for each data sample, like temperature, pressure, humidity, and many more. Interactive visualization and analysis require filtering according to any desired combination of attributes, in particular logical AND operations, which is challenging for large data and many attributes. Many general data structures for this problem are built for and scale with a fixed number of attributes, and scalability of joint queries with arbitrary attribute subsets remains a significant problem. We propose a flexible probabilistic framework for multivariate range queries that decouples all attribute dimensions via projection, allowing any subset of attributes to be queried with full efficiency. Moreover, our approach is output-sensitive, mainly scaling with the cardinality of the query result rather than with the input data size. This is particularly important for joint attribute queries, where the query output is usually much smaller than the whole data set. Additionally, our approach can split query evaluation between user interaction and rendering, achieving much better scalability for interactive visualization than the previous state of the art. Furthermore, even when a multi-resolution strategy is used for visualization, queries are jointly evaluated at the finest data granularity, because our framework does not limit query accuracy to a fixed spatial subdivision.

Abstract We present a novel and light‐weight approach to capture and reconstruct structured 3D models of multi‐room floor plans. Starting from a small set of registered panoramic images, we automatically generate a 3D layout of the rooms and of all the main objects inside. Such a 3D layout is directly suitable for use in a number of real‐world applications, such as guidance, location, routing, or content creation for security and energy management. Our novel pipeline introduces several contributions to indoor reconstruction from purely visual data. In particular, we automatically partition panoramic images in a connectivity graph, according to the visual layout of the rooms, and exploit this graph to support object recovery and rooms boundaries extraction. Moreover, we introduce a plane‐sweeping approach to jointly reason about the content of multiple images and solve the problem of object inference in a top‐down 2D domain. Finally, we combine these methods in a fully automated pipeline for creating a structured 3D model of a multi‐room floor plan and of the location and extent of clutter objects. These contribution make our pipeline able to handle cluttered scenes with complex geometry that are challenging to existing techniques. The effectiveness and performance of our approach is evaluated on both real‐world and synthetic models.

Voxelized representations of complex 3D scenes are widely used nowadays to accelerate visibility queries in many GPU rendering techniques. Since GPU memory is limited, it is important that these data structures can be kept within a strict memory budget. Recently, directed acyclic graphs (DAGs) have been successfully introduced to compress sparse voxel octrees (SVOs), but they are limited to sharing identical regions of space. In this paper, we show that a more efficient lossless compression of geometry can be achieved, while keeping the same visibility-query performance, by merging subtrees that are identical through a similarity transform, and by exploiting the skewed distribution of references to shared nodes to store child pointers using a variabile bit-rate encoding. We also describe how, by selecting plane reflections along the main grid directions as symmetry transforms, we can construct highly compressed GPU-friendly structures using a fully out-of-core method. Our results demonstrate that state-of-the-art compression and real-time tracing performance can be achieved on high-resolution voxelized representations of real-world scenes of very different characteristics, including large CAD models, 3D scans, and typical gaming models, leading, for instance, to real-time GPU in-core visualization with shading and shadows of the full Boeing 777 at sub-millimetric precision.

Abstract We propose a new technique for in‐core and out‐of‐core GPU ray tracing using a generalization of hierarchical occlusion culling in the style of the CHC++ method. Our method exploits the rasterization pipeline and hardware occlusion queries in order to create coherent batches of work for localized shader‐based ray tracing kernels. By combining hierarchies in both ray space and object space, the method is able to share intermediate traversal results among multiple rays. We exploit temporal coherence among similar ray sets between frames and also within the given frame. A suitable management of the current visibility state makes it possible to benefit from occlusion culling for less coherent ray types like diffuse reflections. Since large scenes are still a challenge for modern GPU ray tracers, our method is most useful for scenes with medium to high complexity, especially since our method inherently supports ray tracing highly complex scenes that do not fit in GPU memory. For in‐core scenes our method is comparable to CUDA ray tracing and performs up to5.94× better than pure shader‐based ray tracing.

We present a robust approach for reconstructing the main architectural structure of complex indoor environments given a set of cluttered 3D input range scans. Our method uses an efficient occlusion-aware process to extract planar patches as candidate walls, separating them from clutter and coping with missing data, and automatically extracts the individual rooms that compose the environment by applying a diffusion process on the space partitioning induced by the candidate walls. This diffusion process, which has a natural interpretation in terms of heat propagation, makes our method robust to artifacts and other imperfections that occur in typical scanned data of interiors. For each room, our algorithm reconstructs an accurate polyhedral model by applying methods from robust statistics. We demonstrate the validity of our approach by evaluating it on both synthetic models and real-world 3D scans of indoor environments.

Abstract We introduce a novel efficient technique for automatically transforming a generic renderable 3D scene into a simple graph representation named ExploreMaps, where nodes are nicely placed point of views, called probes, and arcs are smooth paths between neighboring probes. Each probe is associated with a panoramic image enriched with preferred viewing orientations, and each path with a panoramic video. Our GPU‐accelerated unattended construction pipeline distributes probes so as to guarantee coverage of the scene while accounting for perceptual criteria before finding smooth, good looking paths between neighboring probes. Images and videos are precomputed at construction time with off‐line photorealistic rendering engines, providing a convincing 3D visualization beyond the limits of current real‐time graphics techniques. At run‐time, the graph is exploited both for creating automatic scene indexes and movie previews of complex scenes and for supporting interactive exploration through a low‐DOF assisted navigation interface and the visual indexing of the scene provided by the selected viewpoints. Due to negligible CPU overhead and very limited use of GPU functionality, real‐time performance is achieved on emerging web‐based environments based on WebGL even on low‐powered mobile devices.

We present a robust approach for reconstructing the architectural structure of complex indoor environments given a set of cluttered input scans. Our method first uses an efficient occlusion-aware process to extract planar patches as candidate walls, separating them from clutter and coping with missing data. Using a diffusion process to further increase its robustness, our algorithm is able to reconstruct a clean architectural model from the candidate walls. To our knowledge, this is the first indoor reconstruction method which goes beyond a binary classification and automatically recognizes different rooms as separate components. We demonstrate the validity of our approach by testing it on both synthetic models and real-world 3D scans of indoor environments.