Compression of Static and Dynamic 3D Surface Meshes

Mamou, Khaled; Prêteux, Françoise; Prost, R.; Valette, Sébastien

doi:10.1002/9780470611159.ch9

Cited by 1 publication

(2 citation statements)

References 42 publications

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“…Computer animation, as an important subfield of computer graphics, has attracted great attention among these domains. It has revolutionized movies, animation, and video games industry and is incorporated in other fields like flight simulation, arts, marketing, sciences, and scientific visualization .…”

Section: Introductionmentioning

confidence: 99%

“…The most common way of modeling the shape of these objects is the utilization of irregular polygonal meshes that are constructed from a set of connected faces at some vertices. There are different geometric representations such as parametric surfaces, implicit surfaces, point‐sampled surfaces, and voxel‐based representation . Triangle mesh is the most common representation for geometric data because the majority of 3D modeling tools and visualization systems support this form of data.…”

Section: Introductionmentioning

confidence: 99%

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Predictive compression of animated 3D models by optimized weighted blending of key‐frames

Hajizadeh

Ebrahimnezhad

2015

Computer Animation & Virtual

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Efficient compression techniques are required for animated mesh sequences with fixed connectivity and time‐varying geometry. In this paper, we propose a key‐frame‐based technique for three‐dimensional dynamic mesh compression. First, key‐frames are extracted from the animated sequence. Extracted key‐frames are then linearly combined using blending weights to predict the vertex locations of the other frames. These blending weights play a key role in the proposed algorithm because the prediction performance and the required number of key‐frames greatly depend on these weights. We present a novel method in order to compute the optimum blending weight that makes it possible to predict location of the vertices of the non‐key frames with the minimum number of key‐frames. The residual prediction errors are finally quantized and encoded using Huffman coding and another heuristic method. Experimental results on different test sequences with various sizes, topologies, and geometries demonstrate the privileged performance of the proposed method compared with the previous techniques. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%