Digital Bas-Relief from 3D Scenes 何 会 珍

Tim Weyrich Assistant Professor Dept. of Computer Science University College London Connelly Barnes Graduate Student Princeton Graphics Group Princeton University

Szymon Rusinkiewicz Associate Professor Department of Computer Science Princeton University Adam Finkelstein Associate Professor Department of Computer Science Princeton University Jia Deng Ph.D. Student Computer Science Department Princeton University

Examples of bas-relief Top: Ancient Greek, Assyrian relief. Below: Modern examples

Examples of high relief high-relief of the cloister and a rotated view of it

Motivation 1.Given: Height-Data as Z = h(x,y) 2.Want: Relief 3.Need: Compressing Height-Range original 3D modelgenerated relief

Related Work Automatic Generation Of Bas- And High-Reliefs; Cignoni, Montani, Scopigno; Journal of Graphics Tools;1997. Gradient Domain High Dynamic Range Compression Fattal, R., Lischinski, D., Werman, M ACM Transactions on Graphics (SIGGRAPH ’02) Numerous Works On HDR Image Compression

1.Depth : achieved by perspective foreshortening 2.Object Order: preserve depth order of overlapping objects 3.Compression: the background are flatter than those in the foreground 4.Discontinuities: depth discontinuities in the relief become smaller 5.Steps: small step along the object outline 6.Materials: wood, clay, stucco, metal, stone, ivory, bone, ice

Main Idea Transforms the input 3D scene into a regularly sampled height field Compression takes place in Gradient Domain Integration to recover a height field Cignoni : Linear Scaling  important Features are lost originLinear ScalingOur algorithm

Algorithm Workflow GradientExtraction Fix the gradientdirectionExtractingsilhouettes CompressGradientmagtitudeIntegration

1.Retrieving depth values: perspective foreshortening 2.Gradient Extraction: forward difference to extract gradients 3.Why Fix the gradient direction? preserve shapes visible 4. silhouettes: contribute as overly large gradients 5. How to extract silhouettes

bunnyextracted silhouettes sprrow extracted silhouettes

6.Attenuate gradient magtitude nonlinear compression function C to the gradient magnitude

this algorithmlinear algorithm origin teapotsilhouette

7.Integration: optimization process

Poisson equation:

Results about different

8.Artistic Controls: decompose the gradient field

Left : Increasing low frequencies. Right : Amplify high frequencies Results:

Left: unmodified version. Right: emphasize the teapot

Results:

Left: Scenes with high complexityRight: emphasizing depth discontinuities

Future Work 1.Other shapes: Creation of alto-relievo 2.Explore material properties into the algorithm 3. Using normal as input Top: Photograph of a pine cone, and its normal field Bottom: Relief after converting normals to gradients

Thank you!