Computers & Graphics-Uk最新文献

The display coefficients that produce the signal emitted by a light field display are usually calculated to approximate the radiance over a set of sampled rays in the light field space. However, not all information contained in the light field signal is of equal importance to an observer. We propose a retinal pre-filtering of the light field samples that takes into account the image formation process of the observer to determine display coefficients that will ultimately produce better retinal images for a range of focus distances. We demonstrate a significant increase in image definition without changing the display resolution.

Water and light interactions cause color shifts and blurring in underwater images, while dynamic underwater illumination further disrupts scene consistency, resulting in poor performance of optical image-based reconstruction methods underwater. Although Neural Radiance Fields (NeRF) can describe aqueous medium through volume rendering, applying it directly underwater may induce artifacts and floaters. We propose SP-SeaNeRF, which uses micro MLP to predict water column parameters and simulates the degradation process as a combination of real colors and scattered colors in underwater images, enhancing the model’s perception of scattering. We use illumination embedding vectors to learn the illumination bias within the images, in order to prevent dynamic illumination from disrupting scene consistency. We have introduced a novel sampling module, which focuses on maximum weight points, effectively improves training and inference speed. We evaluated our proposed method on SeaThru-NeRF and Neuralsea underwater datasets. The experimental results show that our method exhibits superior underwater color restoration ability, outperforming existing underwater NeRF in terms of reconstruction quality and speed.

Virtual Humans (VHs) emerged over 50 years ago and have since experienced notable advancements. Initially, developing and animating VHs posed significant challenges. However, modern technology, both commercially available and freely accessible, has democratized the creation and animation processes, making them more accessible to users, programmers, and designers. These advancements have led to the replication of authentic traits and behaviors of real actors in VHs, resulting in visually convincing and behaviorally lifelike characters. As a consequence, many research areas arise as functional VH technologies. This paper explored the evolution of four areas and emerging trends related to VHs while examining some of the implications and challenges posed by highly realistic characters within these domains.

This research paper explores the impact of augmented reality (AR) tracking characteristics, specifically an AR head-worn display’s tracking registration accuracy and precision, on users’ spatial abilities and subjective perceptions of trust in and reliance on the technology. Our study aims to clarify the relationships between user performance and the different behaviors users may employ based on varying degrees of trust in and reliance on AR. Our controlled experimental setup used a 360° field-of-regard search-and-selection task and combines the immersive aspects of a CAVE-like environment with AR overlays viewed with a head-worn display.

We investigated three levels of simulated AR tracking errors in terms of both accuracy and precision (+0°, +1°, +2°). We controlled for four user task behaviors that correspond to different levels of trust in and reliance on an AR system: AR-Only (only relying on AR), AR-First (prioritizing AR over real world), Real-Only (only relying on real world), and Real-First (prioritizing real world over AR). By controlling for these behaviors, our results showed that even small amounts of AR tracking errors had noticeable effects on users’ task performance, especially if they relied completely on the AR cues (AR-Only). Our results link AR tracking characteristics with user behavior, highlighting the importance of understanding these elements to improve AR technology and user satisfaction.

In this paper, we investigate the suitability of different visual representations of pathological growth and shrinkage using surface models of intracranial aneurysms and liver tumors. By presenting complex medical information in a visually accessible manner, audiences can better understand and comprehend the progression of pathological structures. Previous work in medical visualization provides an extensive design space for visualizing medical image data. However, determining which visualization techniques are appropriate for a general audience has not been thoroughly investigated.

We conducted a user study (n = 40) to evaluate different visual representations in terms of their suitability for solving tasks and their aesthetics. We created surface models representing the evolution of pathological structures over multiple discrete time steps and visualized them using illumination-based and illustrative techniques. Our results indicate that users’ aesthetic preferences largely coincide with their preferred visualization technique for task-solving purposes. In general, the illumination-based technique has been preferred to the illustrative technique, but the latter offers great potential for increasing the accessibility of visualizations to users with color vision deficiencies.

Urban simulations that involve disaster prevention, urban design, and assisted navigation heavily rely on urban geometric models. While large urban areas need a lot of time to be acquired terrestrially, government organizations have already conducted massive aerial LiDAR surveys, some even at the national level. This work aims to provide a pipeline for extracting multi-scale point clouds from 2D building footprints and airborne LiDAR data, which depends on whether the points represent buildings, vegetation, or ground. We denoise the roof slopes, match the vegetation, and roughly recreate the building façades frequently hidden to aerial acquisition using a parametric representation of geometric primitives. We then carry out multiple-scale samplings of the urban geometry until a 3D urban representation can be achieved because we annotate the new version of the original point cloud with the parametric equations representing each part. We mainly tested our methodology in a real-world setting – the city of Genoa – which includes historical buildings and is heavily characterized by irregular ground slopes. Moreover, we present the results of urban reconstruction on part of two other cities, Matera, which has a complex morphology like Genoa, and Rotterdam.

Analysis of 3D textures, also known as relief patterns is a challenging task that requires separating repetitive surface patterns from the underlying global geometry. Existing works classify entire surfaces based on one or a few patterns by extracting ad-hoc statistical properties. Unfortunately, these methods are not suitable for objects with multiple geometric textures and perform poorly on more complex shapes. In this paper, we propose a neural network for binary segmentation to infer per-point labels based on the presence of surface relief patterns. We evaluated the proposed architecture on a high resolution point cloud dataset, surpassing the state-of-the-art, while maintaining memory and computation efficiency.