Fast collision detection between massive models using dynamic simplification

Yoon, Sung‐Eui; Salomon, Brian; Manocha, Dinesh

doi:10.1145/1057432.1057450

Cited by 37 publications

(14 citation statements)

References 21 publications

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“…The detailed feature shape of the interfered mesh can be preserved by implementing t-FFD, and drastic progress of the computational speed can be expected.  Implementation of efficient mesh interference detection; In the proposal method, the mesh interference is detected in a round-robin, so the computational speed can be progressed by using conventional efficient methods [15][16][17].  Implementation of the sophisticated user interface of the system…”

Section: Discussionmentioning

confidence: 99%

A Shape Design to Avoid Interference based on Tensegric Modeling

Ootsubo¹,

Kobayashi²

2010

Computer-Aided Design and Applications

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A new mesh modeling method for shape design to avoid interference between a subject shape and an obstacle shape is proposed. This method is based on the tensegric modeling, and the deformation result is achieved automatically according to few user inputs. Tensegric model is generated on a mesh model of subject shape and simulates pseudo-physical deformation for various constraints. In this method, a disinterfered shape is calculated by two types of methods according to the obstacle representation; "potential based avoidance" and "mesh based avoidance". The deformation result fits to the obstacle shape smoothly. So, this method is effective for cover shape design of complex shapes, e.g. mechanical product, and is applicable for soft tissue deformation, e.g. human body.

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Section: Discussionmentioning

confidence: 99%

A Shape Design to Avoid Interference based on Tensegric Modeling

Ootsubo¹,

Kobayashi²

2010

Computer-Aided Design and Applications

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“…Approximate collision detection is supported during edition and dragging of the selected objects in a hierarchical way (see Figure 4, bottom). This is reminiscent of [YSLM04] in that bounding boxes are rotated with the objects. However, we deal with relatively simple objects and are therefore able to construct the collision hierarchy for the selected objects on the fly, instead of requiring a long pre-process.…”

Section: Visualization and Interactive Scene Editingmentioning

confidence: 99%

Interactive inspection of complex multi-object industrial assemblies

Argudo¹,

Besora²,

Brunet³

et al. 2016

Computer-Aided Design

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The use of virtual prototypes and digital models containing thousands of individual objects is commonplace in complex industrial applications like the cooperative design of huge ships. Designers are interested in selecting and editing specific sets of objects during the interactive inspection sessions. This is however not supported by standard visualization systems for huge models. In this paper we discuss in detail the concept of rendering front in multiresolution trees, their properties and the algorithms that construct the hierarchy and efficiently render it, applied to very complex CAD models, so that the model structure and the identities of objects are preserved. We also propose an algorithm for the interactive inspection of huge models which uses a rendering budget and supports selection of individual objects and sets of objects, displacement of the selected objects and real-time collision detection during these displacements. Our solution -based on the analysis of several existing view-dependent visualization schemes-uses a Hybrid Multiresolution Tree that mixes layers of exact geometry, simplified models and impostors, together with a time-critical, view-dependent algorithm and a Constrained Front. The algorithm has been successfully tested in real industrial environments; the models involved are presented and discussed in the paper.

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“…The method used in this paper follows an efficient two-step collision detection process, which can be considered a simple example of a multiresolution static collision detection algorithm [20]. In this case, only two levels of detail are used: bounding box level and detailed mesh level.…”

Section: A Human Kinematics Filtermentioning

confidence: 99%

Model-Based Reinforcement of Kinect Depth Data for Human Motion Capture Applications

Calderita

Bandera

Bustos

et al. 2013

Sensors

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Motion capture systems have recently experienced a strong evolution. New cheap depth sensors and open source frameworks, such as OpenNI, allow for perceiving human motion on-line without using invasive systems. However, these proposals do not evaluate the validity of the obtained poses. This paper addresses this issue using a model-based pose generator to complement the OpenNI human tracker. The proposed system enforces kinematics constraints, eliminates odd poses and filters sensor noise, while learning the real dimensions of the performer's body. The system is composed by a PrimeSense sensor, an OpenNI tracker and a kinematics-based filter and has been extensively tested. Experiments show that the proposed system improves pure OpenNI results at a very low computational cost.

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Fast collision detection between massive models using dynamic simplification

Abstract: We present a novel approach for collision detection between large models composed of tens of millions of polygons. Each model is represented as a clustered hierarchy of progressive meshes (CHPM

Cited by 37 publications

References 21 publications

A Shape Design to Avoid Interference based on Tensegric Modeling

A Shape Design to Avoid Interference based on Tensegric Modeling

Interactive inspection of complex multi-object industrial assemblies

Model-Based Reinforcement of Kinect Depth Data for Human Motion Capture Applications

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