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Record W2307219311 · doi:10.14288/1.0302113

Model repair and editing tools

2011· article· en· W2307219311 on OpenAlexaff
Vladislav Kraevoy

Bibliographic record

VenuecIRcle (University of British Columbia) · 2011
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceUSablePipeline (software)Set (abstract data type)Range (aeronautics)Polygon meshGraphics pipelineTexture mappingArtificial intelligenceComputer graphicsPopulationComputer visionComputer graphics (images)Data mining3D computer graphicsEngineering

Abstract

fetched live from OpenAlex

With the declining production cost and improvement of scanning technology, three-dimensional model acquisition systems are rapidly becoming more affordable. At the same time, personal computers with graphics hardware capable of displaying complex 3D models have become inexpensive enough to be available to a large population. As a result, there is, potentially, an opportunity to consider new virtual reality uses from areas as diverse as cultural heritage exploration and retail sales applications that will allow people to view associated large classes of realistic 3D objects on home computers and media devices. Although there are many physical techniques for acquiring 3D data, including laser scanners, CT or MRI scans, the basic pipeline of operations (Figure 1.1) lacks a sufficient set of tools to take the acquired data and produce a usable 3D model. This dissertation proposes a set of efficient and robust 3D data reconstruction and editing tools for such a pipeline. We look at the fundamental problems of range scan data completion, modeling, and parameterization. We propose a new cross-parameterization method for efficient calculation of a low-distortion bijective mapping between models. Recent research in digital geometry processing suggests multiple new applications for such a mapping, including pair-wise model editing [11] transferring texture and surface properties (BRDFs, normal maps, etc) [61], and fitting template meshes to multiple data sets [7, 55]. We also extend our cross-parameterization technique to support models with gaps and holes. This allows us to develop a new and robust method for template-based range scan data completion. One of the most significant obstacles in computer graphics is providing easy-to-use tools for creating and editing detailed 3D models. To this end, we present a new set of tools with which non-expert user can create detailed geometric models quickly and easily. In particular, we propose a new modeling system for creating new, original models by mixing and matching parts of pre-existing models. In this way, we eliminate the need for a user to perform complex geometric operations, and thereby reduce the modeling process to that of part selection. This dissertation also proposes a new technique for image-based modeling that allows a user to easily transform a sketch or picture into a 3D model using a 3D template model. The 3D template provides the geometric detail that cannot be inferred from an image alone. This allows the user to create detailed geometric models from pictures alone. We also introduce a real-time editing algorithm that allows the creation of new models through the deformation of existing ones. Our proposed editing algorithm has applications in such common geometric operations as mesh deformation, morphing, and blending. Thus, we propose contributions to the model repair and editing pipeline that simplifies the task of creating and repairing detailed 3D models.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0060.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0480.027

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 head0.027
GPT teacher head0.192
Teacher spread0.165 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreSoftware

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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