Carlos Cadavid

Dr. Carlos Cadavid


Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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18 January 2016 in International Journal on Interactive Design and Manufacturing (IJIDeM)
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DPDaniel Mejia Parra
Daniel Mejia Parra
OROscar Ruiz-Salguero
Oscar Ruiz-Salguero
CCCarlos Cadavid
Carlos Cadavid

1 March 2013 in Journal of Symbolic Computation
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Journal Article
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CCCarlos Cadavid
Carlos Cadavid
SMS. Molina
S. Molina
JVJ.D. Vélez
J.D. Vélez

13 August 2020 in Mathematics
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JPJuan Pareja-Corcho
Juan Pareja-Corcho
OBOscar Betancur-Acosta
Oscar Betancur-Acosta
Jorge PosadaJorge Posada
Jorge Posada
Antonio TammaroAntonio Tammaro
Antonio Tammaro
OROscar Ruiz-Salguero
Oscar Ruiz-Salguero
CCCarlos Cadavid
Carlos Cadavid
Feature Recognition (FR) in Computer-aided Design (CAD) models is central for Design and Manufacturing. FR is a problem whose computational burden is intractable (NP-hard), given that its underlying task is the detection of graph isomorphism. Until now, compromises have been reached by only using FACE-based geometric information of prismatic CAD models to prune the search domain. Responding to such shortcomings, this manuscript presents an interactive FR method that more aggressively prunes the search space with reconfigurable geometric tests. Unlike previous approaches, our reconfigurable FR addresses curved EDGEs and FACEs. This reconfigurable approach allows enforcing arbitrary confluent topologic and geometric filters, thus handling an expanded scope. The test sequence is itself a graph (i.e., not a linear or total-order sequence). Unlike the existing methods that are FACE-based, the present one permits combinations of topologies whose dimensions are two (SHELL or FACE), one (LOOP or EDGE), or 0 (VERTEX). This system has been implemented in an industrial environment, using icon graphs for the interactive rule configuration. The industrial instancing allows industry based customization and itis faster when compared to topology-based feature recognition. Future work is required in improving the robustness of search conditions, treating the problem of interacting or nested features, and improving the graphic input interface.

25 September 2010 in The Visual Computer
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OROscar Ruiz-Salguero
Oscar Ruiz-Salguero
CVC. Vanegas
C. Vanegas
CCCarlos Cadavid
Carlos Cadavid

19 October 2014 in International Journal on Interactive Design and Manufacturing (IJIDeM)
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Journal Article
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DADiego A. Acosta
Diego A. Acosta
OROscar Ruiz-Salguero
Oscar Ruiz-Salguero
SASantiago Arroyave
Santiago Arroyave
RERoberto Ebratt
Roberto Ebratt
CCCarlos Cadavid
Carlos Cadavid
JLJuan J. Londono
Juan J. Londono

17 August 2019 in Mathematics
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DPDaniel Mejia Parra
Daniel Mejia Parra
Jairo SánchezJairo Sánchez
Jairo Sánchez
Jorge PosadaJorge Posada
Jorge Posada
OROscar Ruiz-Salguero
Oscar Ruiz-Salguero
CCCarlos Cadavid
Carlos Cadavid
In the context of CAD, CAM, CAE, and reverse engineering, the problem of mesh parameterization is a central process. Mesh parameterization implies the computation of a bijective map ϕ from the original mesh M ∈ R 3 to the planar domain ϕ ( M ) ∈ R 2 . The mapping may preserve angles, areas, or distances. Distance-preserving parameterizations (i.e., isometries) are obviously attractive. However, geodesic-based isometries present limitations when the mesh has concave or disconnected boundary (i.e., holes). Recent advances in computing geodesic maps using the heat equation in 2-manifolds motivate us to revisit mesh parameterization with geodesic maps. We devise a Poisson surface underlying, extending, and filling the holes of the mesh M. We compute a near-isometric mapping for quasi-developable meshes by using geodesic maps based on heat propagation. Our method: (1) Precomputes a set of temperature maps (heat kernels) on the mesh; (2) estimates the geodesic distances along the piecewise linear surface by using the temperature maps; and (3) uses multidimensional scaling (MDS) to acquire the 2D coordinates that minimize the difference between geodesic distances on M and Euclidean distances on R 2 . This novel heat-geodesic parameterization is successfully tested with several concave and/or punctured surfaces, obtaining bijective low-distortion parameterizations. Failures are registered in nonsegmented, highly nondevelopable meshes (such as seam meshes). These cases are the goal of future endeavors.