UNC Dynamic Scene Benchmarks

1. Introduction

We provide a few dynamic datasets, which have been used in the following paper:

Ray Tracing Dynamic Scenes using Selective Restructuring,

Sung-eui Yoon, Sean Curtis, and Dinesh Manocha

Eurographics Symposium on Rendering 2007

Fast Proximity Computation Among Deformable Models using Discrete Voronoi Diagrams ,

Avneesh Sud, Naga Govindaraju, Russell Gayle, Ilknur Kabul, and Dinesh Manocha

ACM SIGGRAPH 2006

Particularly, our benchmarks have high model complexity or non-rigid deformations or breaking objects.

All the dynamic scenes are stored in the ply file format; also, each dynamic frame is stored in a separate ply file with different ID. Sequences of each benchmark are shown in the MPEG4 video of the paper mentioned above. Please contact Sung-eui Yoon or Dinesh Manocha, if you have questions.

2. Dynamic Benchmarks

A. Exploding dragon and bunny model

# of triangles: 252K

# of raw sequences: 16

Zipped file size: 48MB, download

Characteristics: one or more objects break into smaller pieces (i.e. explosions) and thereby change their topologies.

B. Cloth simulation model

# of triangles: 92K

# of raw sequences: 94

Zipped file size: 102MB download

Characteristics: it has non-rigid deformation.

C. N-body simulation

# of triangles: 146K

# of raw sequences: 94

Zipped file size: 78MB download

Characteristics: this benchmark consists of multiple moving objects. Each object may undergo a rigid or deformable motion and the objects collide with each other and the ground.

D. Letters

# of triangles: 5K

# of raw sequences: 475

Zipped file size: 23MB download

Characteristics: Multiple deforming models of alphabet and number shapes that fall into a bowl and break into pieces.

Acknowledgements

We would like to thank Naga Govindaraju, Ilknur Kabul, and Stephane Redon for the exploding dragon/ bunny model and the cloth simulation model. Also, we would like to thank Sean Curtis for the N-body benchmark.

Other developers include Sung-eui Yoon, Russ Gayle, Avneesh Sud, Sean Curtis, Min Tang, Ming Lin, and Dinesh Manocha.