Reference View Research Articles

PERF: Panoramic Neural Radiance Field From a Single Panorama.

Neural Radiance Field (NeRF) has achieved substantial progress in novel view synthesis given multi-view images. Recently, some works have attempted to train a NeRF from a single image with 3D priors. They mainly focus on a limited field of view with a few occlusions, which greatly limits their scalability to real-world 360-degree panoramic scenarios with large-size occlusions. In this paper, we present PERF, a 360-degree novel view synthesis framework that trains a panoramic neural radiance field from a single panorama. Notably, PERF allows 3D roaming in a complex scene without expensive and tedious image collection. To achieve this goal, we propose a novel collaborative RGBD inpainting method and a progressive inpainting-and-erasing method to lift up a 360-degree 2D scene to a 3D scene. Specifically, we first predict a panoramic depth map as initialization given a single panorama and reconstruct visible 3D regions with volume rendering. Then we introduce a collaborative RGBD inpainting approach into a NeRF for completing RGB images and depth maps from random views, which is derived from an RGB Stable Diffusion model and a monocular depth estimator. Finally, we introduce an inpainting-and-erasing strategy to avoid inconsistent geometry between a newly-sampled view and reference views. The two components are integrated into the learning of NeRFs in a unified optimization framework and achieve promising results. Extensive experiments on Replica and a new dataset PERF-in-the-wild demonstrate the superiority of our PERF over state-of-the-art methods. Our PERF can be widely used for real-world applications, such as panorama-to-3D, text-to-3D, and 3D scene stylization applications.

An unjustly neglected theory of semantic reference

AbstractThere is a simple, intuitive theory of the semantic reference of proper names that has been unjustly neglected. This is the view that semantic reference is conventionalized speakers reference, i.e. the view that a name semantically refers to an object if, and only if, there exists a convention to use the name to speaker-refer to that object. The theory can be found in works dealing primarily with other issues (e.g. Stine in Philos Stud 33:319–337, 1977; Schiffer in Erkenntnis 13:171–206, 1978; Sainsbury in Erkenntnis 80:195–214, 2015; Sainsbury, Thinking about things, Oxford University Press, 2018), yet these authors provide no sustained discussion of it. Devitt (Designation, Columbia University Press, 1981) did formulate a view on which semantic reference is conventionalized speaker’s reference, yet his views are assimilated to causalist views. This is a mistake. While the conventionalized speaker’s reference view captures much of what is plausible in descriptivism and causalism, it remains distinct from both.

Managementul psihologic al bolnavilor cronici

In this article we explore how people adapt to chronic illness through cognitive strategies, coping and emotional regulation. Being concerned about the degree of coverage of the field from the point of view of bibliographic references, we considered going through a literature review to be the best and fastest way to be informed about the dynamics of the chosen field. In this regard, we will present an analysis of the MEDLINE and PUBMED databases of the National Library of Medicine. The data obtained were systematized, analyzed and presented in the form of a narrative synthesis.

View synthesis with multiplane images from computationally generated RGB-D light fields

Image based view synthesis using deep neural networks provide novel scene views from a set of captured single or multiple images. Multiplane images (MPI) represent scene content as set of RGBα planes within a reference view frustum and render novel views by projecting the content into the target viewpoints. Image based view synthesis with multiple images is very popularly deployed in various areas because it effectively represents geometric uncertainty in ambiguous regions and can convincingly simulate non-Lambertian effects. However, previous image based view synthesis approaches suffer from interpolating and extrapolating information in pixels or ray spaces to generate seamless novel views without occlusion. To effectively improve visual performance for view interpolation and extrapolation, this paper proposes a novel view synthesis with MPI images. From a monocular RGB image, light field images are computationally generated, the proposed depth map guided deep network produces robust MPI using the light field images and their corresponding depth images, and the MPI network embedded with depth attention blocks forces semantic and geometric information to be uniformly distributed and divided among layers. The proposed approach achieves 3.5% and 4.02% improvements in SSIM and PSNR values, comparing to the SOTA approaches. Qualitative analysis on benchmark dataset also verifies the robustness of the proposed approach.

Coarse- and Fine-Grained Fusion Hierarchical Network for Hole Filling in View Synthesis.

Depth image-based rendering (DIBR) techniques play an essential role in free-viewpoint videos (FVVs), which generate the virtual views from a reference 2D texture video and its associated depth information. However, the background regions occluded by the foreground in the reference view will be exposed in the synthesized view, resulting in obvious irregular holes in the synthesized view. To this end, this paper proposes a novel coarse and fine-grained fusion hierarchical network (CFFHNet) for hole filling, which fills the irregular holes produced by view synthesis using the spatial contextual correlations between the visible and hole regions. CFFHNet adopts recurrent calculation to learn the spatial contextual correlation, while the hierarchical structure and attention mechanism are introduced to guide the fine-grained fusion of cross-scale contextual features. To promote texture generation while maintaining fidelity, we equip CFFHNet with a two-stage framework involving an inference sub-network to generate the coarse synthetic result and a refinement sub-network for refinement. Meanwhile, to make the learned hole-filling model better adaptable and robust to the "foreground penetration" distortion, we trained CFFHNet by generating a batch of training samples by adding irregular holes to the foreground and background connection regions of high-quality images. Extensive experiments show the superiority of our CFFHNet over the current state-of-the-art DIBR methods. The source code will be available at https://github.com/wgc-vsfm/view-synthesis-CFFHNet.

Light Field Neural Rendering.

Classical light field rendering for novel view synthesis can accurately reproduce view-dependent effects such as reflection, refraction, and translucency, but requires a dense view sampling of the scene. Methods based on geometric reconstruction need only sparse views, but cannot accurately model non-Lambertian effects. We introduce a model that combines the strengths and mitigates the limitations of these two directions. By operating on a four-dimensional representation of the light field, our model learns to represent view-dependent effects accurately. By enforcing geometric constraints during training and inference, the scene geometry is implicitly learned from a sparse set of views. Concretely, we introduce a two-stage transformer-based model that first aggregates features along epipolar lines, then aggregates features along reference views to produce the color of a target ray. Additionally, we propose modifications that allow the model to generalize to scenes without any fine-tuning. Our model outperforms the state-of-the-art on multiple forward-facing and 360 ° datasets, with larger margins on scenes with severe view-dependent variations. Code and results can be found at https://light-field-neural-rendering.github.io/.

Multi-View Stereo Network Based on Attention Mechanism and Neural Volume Rendering

Due to the presence of regions with weak textures or non-Lambertian surfaces, feature matching in learning-based Multi-View Stereo (MVS) algorithms often leads to incorrect matches, resulting in the construction of the flawed cost volume and incomplete scene reconstruction. In response to this limitation, this paper introduces the MVS network based on attention mechanism and neural volume rendering. Firstly, we employ a multi-scale feature extraction module based on dilated convolution and attention mechanism. This module enables the network to accurately model inter-pixel dependencies, focusing on crucial information for robust feature matching. Secondly, to mitigate the impact of the flawed cost volume, we establish a neural volume rendering network based on multi-view semantic features and neural encoding volume. By introducing the rendering reference view loss, we infer 3D geometric scenes, enabling the network to learn scene geometry information beyond the cost volume representation. Additionally, we apply the depth consistency loss to maintain geometric consistency across networks. The experimental results indicate that on the DTU dataset, compared to the CasMVSNet method, the completeness of reconstructions improved by 23.1%, and the Overall increased by 7.3%. On the intermediate subset of the Tanks and Temples dataset, the average F-score for reconstructions is 58.00, which outperforms other networks, demonstrating superior reconstruction performance and strong generalization capability.

Progressive refinement imaging with depth-assisted disparity correction

In recent years, the increasing on-board compute power of mobile camera devices gave rise to a class of digitization algorithms that dynamically fuse a stream of camera observations into a progressively updated scene representation. Previous algorithms either obtain general 3D surface representations, often exploiting range maps from a depth camera, such as, Kinect Fusion, etc.; or they reconstruct planar (or distant spherical, respectively) 2D images with respect to a single (perspective or orthographic) reference view, such as, panoramic stitching or aerial mapping. Our work sets out to combine aspects of both, reconstructing a 2.5-D representation (color and depth) as seen from a fixed viewpoint, at spatially variable resolution. Inspired by previous work on “progressive refinement imaging”, we propose a hierarchical representation that enables progressive refinement of both colors and depths by ingesting RGB-D images from a handheld depth camera that is carried through the scene. We evaluate our system by comparing it against state-of-the-art methods in 2D progressive refinement and 3D scene reconstruction, using high-detail indoor and outdoor data sets comprising medium to large disparities. As we will show, the restriction to 2.5-D from a fixed viewpoint affords added robustness (particularly against self-localization drift, as well as backprojection errors near silhouettes), increased geometric and photometric fidelity, as well as greatly improved storage efficiency, compared to more general 3D reconstructions. We envision that our representation will enable scene exploration with realistic parallax from within a constrained range of vantage points, including stereo pair generation, visual surface inspection, or scene presentation within a fixed VR viewing volume.

An exhaustive analysis of post-traumatic brain injury dementia using bibliometric methodologies.

It is widely accepted that traumatic brain injury (TBI) increases the risk of developing long-term dementia, although some controversies surrounding this topic exist. Annually, approximately 69 million individuals suffer from TBI all around the world. Such a large population of TBI patients could lead to a future surge in the number of dementia patients. Due to the potentially severe consequences of TBI, various research projects on post-TBI dementia have emerged worldwide. Therefore, it is essential to comprehend the current status and development of post-TBI dementia for future research. The purpose of the study was to provide an overview of the field and identify hotspots, research frontiers, and future research trends for post-TBI dementia. Articles related to post-TBI dementia were retrieved from the Web of Science Core Collection for the period between 2007 and 2022, and analyzing them based on factors such as citations, authors, institutions, countries, journals, keywords, and references. Data analysis and visualization were conducted using VOSviewer, CiteSpace, and an online bibliometric platform (https://bibliometric.com). From 2007 to 2022, we obtained a total of 727 articles from 3,780 authors and 1,126 institutions across 52 countries, published in 262 journals. These articles received a total of 29,353 citations, citing 25,713 references from 3,921 journals. Over the last 15 years, there has been a significant upward trend in both publications and citations. The most productive country was the United States, the most productive institution was Boston University, and the most productive author was McKee AC. Journal of Neurotrauma has been identified as the periodical with the greatest number of publications. Three clusters were identified through cluster analysis of keywords. A burst in the use of the term "outcome" in 2019 is indicative of a future research hotspot. The timeline view of references showed 14 clusters, of which the first 4 clusters collected the majority of papers. The first 4 clusters were "chronic traumatic encephalopathy," "age of onset," "tauopathy," and "cognitive decline," respectively, suggesting some areas of interest in the field. The subject of post-TBI dementia has raised much interest from scientists. Notably, America is at the forefront of research in this area. Further collaborative research between different countries is imperative. Two topical issues in this field are "The association between TBI and dementia-related alterations" and "chronic traumatic encephalopathy (CTE)." Studies on clinical manifestation, therapy, pathology, and pathogenic mechanisms are also popular in the field.

MAP3D: An explorative approach for automatic mapping of real-world eye-tracking data on a virtual 3D model.

Mobile eye tracking helps to investigate real-world settings, in which participants can move freely. This enhances the studies' ecological validity but poses challenges for the analysis. Often, the 3D stimulus is reduced to a 2D image (reference view) and the fixations are manually mapped to this 2D image. This leads to a loss of information about the three-dimensionality of the stimulus. Using several reference images, from different perspectives, poses new problems, in particular concerning the mapping of fixations in the transition areas between two reference views. A newly developed approach (MAP3D) is presented that enables generating a 3D model and automatic mapping of fixations to this virtual 3D model of the stimulus. This avoids problems with the reduction to a 2D reference image and with transitions between images. The x, y and z coordinates of the fixations are available as a point cloud and as .csv output. First exploratory application and evaluation tests are promising: MAP3D offers innovative ways of post-hoc mapping fixation data on 3D stimuli with open-source software and thus provides cost-efficient new avenues for research.

Virtual view synthesis using joint information from multi-view

Immersive media has attracted widespread attention with the development of virtual reality. Three Degree of Freedom Plus media greatly enhances the user experience by allowing users’ head motion and viewpoint switching within a certain range. Due to the limitation of panoramic video acquisition and transmission, it is impossible to obtain videos from any viewpoint directly. Virtual view synthesis is the general solution to this problem. However, existing algorithms do not adequately consider the pixel correlation between multiple views. Thus, we propose a virtual view synthesis algorithm using joint information from multi-view panoramic videos to further explore the pixel correlation. Specifically, sub-pixels from different reference views in the virtual view are obtained by performing multi-view three-dimensional image warping. Dedicated area division and interpolation methods are then designed to improve the synthesized quality. Experimental results show that the proposed algorithm outperforms the state-of-the-art virtual view synthesis algorithms in performance and efficiency.

STUDENTS’ CRITICAL THINKING ABILITY IN ENGLISH TEACHING AND LEARNING

The purpose of this study was to discover how students' critical thinking skills developed during the classroom learning process. This research was conducted at SMA Negeri 1 Ratahan, and the sample is the first-year students, which was made up of 35 students. The instruments used in collecting data are observation techniques. The data were analyzed qualitatively. 1) The concept of purpose was present in Meetings 2, 3, 4, and 5. It is shown that when students reason, they reason to achieve some objective, to satisfy some desire, or to fulfill some need. 2) The concept of "question" was in Meetings 1, 2, 4, and 5. This shows that generally, the students had questions and problems to be solved. (3) The concept of concept was only observed in Meetings 2, 3, and 5. Some ideas or concepts are used in all reasoning but not others. 4) The concept of assumption was in Meetings 1, 2, 4, and 5. All reasoning must begin somewhere; we must take some things for granted. Any "defect" in the assumptions or presuppositions with which the reasoning begins is a possible source of problems for students. 5) Only at Meeting 5 was the concept of information discussed. Whenever we reason, there is some "stuff," some phenomenon, about which we are reasoning. Any "defect," then, in the experiences, data, evidence, or raw material upon which a person’s reasoning is based is a possible source of problems. 6) The concept of interpretation was discussed in Meetings 3 and 4. Reasoning proceeds in steps: "Because this is so, that also is so (or probably so)," or "Since this, therefore that." Any "defect" in such inferences is a possible problem in our reasoning. 7) In Meetings 3 and 5, the concept of point of view or frame of reference was discussed. In this case, students reason within certain frames of reference. Any "defect" in that point of view or frame of reference is a possible source of problems in the reasoning. 8) Only Meetings 3 and 4 discussed the concept of implications (and consequences). Any "defect" in the implications or consequences of our reasoning is a possible source of problems.
 

Context Region Identification Based Quality Assessment of 3D Synthesized Views

Perceptual quality assessment of 3D synthesized views is an open research problem in computer vision. Researchers across the globe have developed several algorithms to identify distortions. At the same time, the existing algorithms cannot quantify the context in which these distortions affect the overall perceptual quality. According to the recently proposed 3D view synthesis algorithm, the choice of context region for the disocclusion plays a vital role in predicting the quality of 3D views. The context region taken from the background of a view produces a perceptually better quality of 3D synthesized views than when the context region is taken from the foreground. With this view, the proposed algorithm aims to identify the context region and incorporate this information for the perceptual quality assessment of 3D synthesized views. We observed that the depth energy maps of the 3D synthesized views vary significantly with the change in the context region and subsequently can identify the context region. Hence, in this work, we propose a new and efficient quality assessment algorithm based upon the variation in the depth of 3D synthesized and reference views, giving two-fold advantages: 1. It can predict the quality based on whether the context region is foreground or not. 2. It is also able to suggest the possible location of distortions. We have proposed two new algorithms for both situations when the context region is foreground or not. The overall predicted score is the direct multiplication of the quality score estimated when the context region is foreground or not. When applied to the established benchmark dataset, the proposed technique performs satisfactorily with the PLCC of 0.7707 and 0.7572 of SRCC. Also, the proposed algorithm can work as a plug-in to improve the performance of the existing algorithms.

Clustering Reference Images Based on Covisibility for Visual Localization

In feature-based visual localization for small-scale scenes, local descriptors are used to estimate the camera pose of a query image. For large and ambiguous environments, learning-based hierarchical networks that employ local as well as global descriptors to reduce the search space of database images into a smaller set of reference views have been introduced. However, since global descriptors are generated using visual features, reference images with some of these features may be erroneously selected. In order to address this limitation, this paper proposes two clustering methods based on how often features appear as well as their covisibility. For both approaches, the scene is represented by voxels whose size and number are computed according to the size of the scene and the number of available 3D points. In the first approach, a voxel-based histogram representing highly reoccurring scene regions is generated from reference images. A meanshift is then employed to group the most highly reoccurring voxels into place clusters based on their spatial proximity. In the second approach, a graph representing the covisibility-based relationship of voxels is built. Local matching is performed within the reference image clusters, and a perspective-n-point is employed to estimate the camera pose. The experimental results showed that camera pose estimation using the proposed approaches was more accurate than that of previous methods.

Folk Intuitions about Reference Change and the Causal Theory of Reference

In this paper, we present and discuss the findings of two experiments about reference change. Cases of reference change have sometimes been invoked to challenge traditional versions of semantic externalism, but the relevant cases have never been tested empirically. The experiments we have conducted use variants of the famous Twin Earth scenario to test folk intuitions about whether natural kind terms such as ‘water’ or ‘salt’ switch reference after being constantly (mis)applied to different kinds. Our results indicate that this is indeed so. We argue that this finding is evidence against Saul Kripke’s causal-historical view of reference, and at least provisional evidence in favor of the causal source view of reference as suggested by Gareth Evans and Michael Devitt.

Dasgupta's Detonation

Dasgupta's Detonation

Dostoevsky and Hesychasm: “Crime and Punishment”

Not much has been said about Dostoevsky and hesychasm, and mainly with the greatest evidence and persuasiveness in the case when direct references to the figures of hesychasm appeared directly in the text of Dostoevsky's novel (“The Brothers Karamazov”). However, hesychasm can be considered as an optimal explanatory structure already for the novel “Crime and Punishment”. In this novel hesychasm is most obviously present, not from the point of view of superficial references or an external plot developing in the “apparent flow of life” (as Dostoevsky designated what happens on the surface of being), but from the point of view of the deepest plot, in which what happens in the novel is connected with “ends and beginnings” (so Dostoevsky called the origins and the results of events that are beyond the obvious, beyond time). The original title of the novel, “The Drunkards”, which later became “Crime and Punishment”, as well as the characteristics of the characters found in “Crime and Punishment” (to be “drunk without wine”, to be mistaken for a drunk in a sober state, to hide behind the illusion of intoxication their pre- and post-criminal state), strictly associate drunkenness with sin and a crime. The direct opposition to this state is sobriety, which the participants of the Hesychast tradition strive to achieve, and the collection of texts of this tradition, “Philokalia”, is called in the translation by Paisii Velichkovsky: “Words and Beginnings of Sacred Sobriety”. The separation of heart and mind, which characterizes the two main criminal characters of the novel, is the main characteristic of the pre-natural state of a person according to hesychasm. Hesychasm also makes it possible to explain why the heroine, who occupies the highest position in the spiritual structure of the novel, is characterized by the words “She will see God”.

Location and patency of the ostium for sinus augmentation from an implant treatment planning perspective: A retrospective CBCT study

<h3>Background</h3> In the posterior maxilla, pneumatization of the maxillary sinus paralleled with resorption of the residual ridge markedly reduces bone volume. Sinus lift/sinus augmentation is usually performed to increase bone volume at these sites. The total height needed for augmentation depends on residual ridge height. The ostium, which is an anatomical conduit that opens into the middle meatus, must be patent; blockage due to inflammation or infection can lead to confinement of infection within the maxillary sinus. Clinicians should be cautious while planning sinus augmentation procedures in patients with radiographically evident sinus disease. <h3>Objective</h3> The objective of this study was to evaluate the location of the ostium with reference to the sinus floor and/or mucosal thickening when present. <h3>Study Design</h3> In total, 200 sino-nasal complexes comprising 100 dentate and 100 edentulous scans were retrospectively assessed using CBCT. The coronal section demonstrating the ostiomeatal complex was selected as a reference view to perform measurements of the sinus. The measurements were made by 2 evaluators. Comparative analyses of measurements were performed between dentate and edentulous patients. <h3>Results</h3> The safe height to which the sinus can be elevated without compromising the integrity of the ostiomeatal complex was calculated for each sinus. In the presence of significant mucosal thickening, the height available was calculated by subtracting the height of mucosal thickening from the sinus floor to the location of the ostium. The available height was approximately 27.05 mm for dentate and 23.40 mm for edentulous patients. The interoperator reliability was excellent for all parameters evaluated. <h3>Conclusion</h3> This retrospective study shows that CBCT is valuable in evaluating the location and patency of the ostium for planning sinus augmentation procedures for dental implant placement. <b>Statement of Ethical Review</b> Ethical Review or exemption was not warranted for this study

Progressive Multi-Scale Fusion Network for Light Field Super-Resolution

Light field (LF) cameras can record multi-view images from a single scene, and these images can provide spatial and angular information to improve the performance of image super-resolution (SR). However, it is a challenge to incorporate distinctive information from different LF views. At the same time, due to the inherent resolution of the image sensor, high spatial and angular resolution are trade-off problems. In this paper, we propose a progressive multi-scale fusion network (PMFN) to improve the LFSR performance. Specifically, a progressive feature fusion block (PFFB) based on an encoder-and-decoder structure is designed to implicitly align disparities and integrate complementary information between complementary views. The core module of the PFFB is a dual-branch multi-scale fusion module (DMFM), which can integrate the information from a reference view and auxiliary views to produce a fusion feature. Each DMFM consists of two parallel branches, which have different receptive fields to fuse hierarchical features from complementary views. Three DMFMs with a dense connection are used in the PFFB, which can fully exploit multi-level features to improve the SR performance. Experimental results on both synthetic and real-world datasets demonstrate that the proposed model achieves state-of-the-art performance among existing methods. Moreover, quantitative results show that our method can also generate faithful details.

Improved inter-view correlations for low complexity MV-HEVC

The more advanced multi-view extension, MV-HEVC, effectively exploits visual similarities between multi-view videos and enables high compression efficiency. Each view in the multi-view sequence depends on the captured scene, the distance between cameras and recording angles. Increasing the distance between dependent viewpoints generates an inter-view disparity. This impacts the inter-view similarities, affects the disparity estimation and further increases the computational complexity of the MV-HEVC encoder. In this paper, an efficient earlier disparity estimation is proposed for low complexity MV-HEVC. This algorithm is based on reducing the complexity of disparity estimation by eliminating the inter-view offset. Moreover, the inter-view similarities are controlled by considering the reliability of each coding unit size in the search range. This reliability is estimated by reducing the number of searching points within a new limited window. For reliable motion estimation, we further proposed an earlier decision of coding units splitting in the dependent views according to those in the reference views. Experimental results show that the proposed algorithm can achieve an average encoding time saving of 20.37%–40,61% with marginal performance degradation.