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Record W4235995323 · doi:10.1002/cav.335

Editorial Issue VRCAI'08

2010· article· en· W4235995323 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueComputer Animation and Virtual Worlds · 2010
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceAnimationUsabilityAugmented realityPascal (unit)SketchComputer graphics (images)Human–computer interactionMultimediaAlgorithmProgramming language

Abstract

fetched live from OpenAlex

This special issue contains the journal extension of the Best Paper, Best Application and other five selected papers of ACM VRCAI 2008. The first paper by Hanhoon Park, NHK Science & Technical Research Laboratories, Japan, Jihyun Oh, Realtimevisual Inc., Byung-Kuk Seo and Jong-Il Park, Hanyang University, Korea, proposes an automatic method for flexibly adjusting the confidence of visual cues in model-based camera tracking. The adjustment is based on the conditions of the target object/scene and the reliability of the initial or previous camera pose. The method can achieve real-time performance and successfully applied to a mobile augmented reality (AR) guidance system for a museum. This paper is the Best Paper of VRCAI 2008. The second paper, by Guangzheng Fei, Communication University of China, Won-Sook Lee, University of Ottawa, Canada, Zijun Xin and Huikai Dong, Communication University of China, and Chris Joslin, Carleton University, Canada, describes an animation creation system called PASCAL that supports sketch based modeling and physics augmented locomotion simultaneously. The system uses sketches and reconfigurable space canvases as basic modeling primitives and uses physics to improve the expressiveness and efficiency of several animation techniques to obtain controllable and plausible locomotion animation. The usability evaluation of the system was conducted both with professional and novice animators. This paper wins the Best Application of VRCAI 2008. In the third paper, Yimin Wang and Jianmin Zheng, from Nanyang Technological University, Singapore, propose an edge-based parameterization method, in which the edges rather than the vertices of the mesh are treated as the target for parameterization. It first parameterizes the edges on the two boundaries of the tubular mesh, then parameterizes the internal edges based on the mean value coordinates, and finally computes the parameters of the mesh vertices. The method does not need cutting of the mesh. It improves conventional cutting-based algorithms, which cut the mesh to make it a disk topologically, and overcomes the problems of cutting paths that are the zigzag paths leading to suboptimal parameterizations and the difficulty in finding good cutting paths. Some applications such as surface fitting and texture mapping are also provided. Jie Zhang, Soh-Khim Ong, and Andrew Yeh-Ching Nee from National University of Singapore, present, in the next paper, an implementation of machining simulation in a real machining environment applying AR technology. This in situ machining simulation system allows a machinist to analyze the simulation process, adjust the machining parameters, and observe the results in real-time in a real machining environment. Such a system is useful for machinists and trainees during the trial and learning stages, allowing them to experiment with different machining parameters on a real machine without having to worry about possibilities of machine and tool breakages. Experiments were conducted on a real 3-axis CNC machine to validate and evaluate the performance of the system and the feedback from a survey carried out with the experiments is very positive. Corey Manders, Farzam Farbiz, Ka Yin Tang, Miaolong Yuan, Gim Guan Chua, and Susanto Rahardja of A*STAR Institute for Infocomm Research, Singapore, present, in the next paper, a system for interacting with 3D objects in a 3D virtual environment. Using the notion that a typical head-mounted display does not cover the user's entire face, they use a fiducial marker placed on the HMD to locate the user's exposed facial skin. Using this information, a skin model is built and combined with the depth information obtained from a stereo camera. The information when used in tandem allows the position of the user's hands to be detected and tracked in real time. Once both hands are located, the system allows the user to manipulate the object with five degrees of freedom (translation in x, y, and z axis with roll and yaw rotations) in virtual three-dimensional space using a series of intuitive hand gestures. In the sixth paper, by Jiejie Zhu and Zhigeng Pan of Beihang University and Zhejiang University, Chao Sun of Beihang University, and Wenzhi Chen of Zhejiang University, China, the authors propose an approach to separate occluded objects in multiple layers by utilizing depth, color, and neighborhood information. Scene depth is obtained by stereo cameras and two Gaussian local kernels are used to represent color and spatial smoothness. These three cues are intelligently fused in a probability framework, where the occlusion information can be safely estimated. Experiment results showed that the approach can correctly register virtual and real objects in different depth layers, and provide a spatial-awareness interaction environment. The seventh paper is also the last paper by Chunyong Ma, Ge Chen, Yong Han, Yongyang Qi, and Yong Chen of Ocean University of China. The paper introduces a virtual city oriented VR-GIS platform which synthesizes several latest information technologies including virtual reality, 3D geographical information system, remote sensing and multi-dimensional visualization. The platform is a seamless integration of VR functions and GIS analysis methods, which can be used to organize and present massive spatial data. It also supplies 3D spatial analysis functions, 3D visualization for spatial process and natural simulation, and serves as an engine platform for digital city. The two last papers will appear in the 5th regular special issue.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.256
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it