Relief: A Modeling By Drawing Tool

Post on 07-Jul-2015

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This paper presents a modeling system which takes advantage of two-dimensional drawing knowledge to design three-dimensional free-form shapes. A set of mouse or tablet strokes is interpreted by the system as defining both a two-dimensional shape boundary and a displacement map. This information is used for pushing or pulling vertices of existing surfaces, or for creating vertices of new surface patches. To relieve the burden of 3D manipulation from the user, patches are automatically positioned in space. The iterative design process alternates a modeling by drawing sequence and a viewpoint change. To stay as close as possible to the traditional drawing experience, the system imposes the minimum number of constraints on the topology of either the strokes set or the resulting surface.

Transcript of Relief: A Modeling By Drawing Tool

Relief: A Modeling by Drawing Tool

David Bourguignon1 Raphaëlle Chaine2

Marie-Paule Cani3 George Drettakis4

1Princeton University / INRIA Rocquencourt 2LIRIS / CNRS / UCBL3GRAVIR / INP Grenoble 4REVES / INRIA Sophia-Antipolis

Outline

• Motivation• Previous Work• Tool Workflow• Reconstruction• Adaptive Sampling & Depth Inference• Tool Interface• Results

On Users

• Most people draw– Writing alternative

• Few people sculpt– Play-Doh days long gone– Materials difficult to handle

Goals

• Use 2D tools to perform 3D operations

Goals

• Use 2D tools to perform 3D operations• Model global and local surface

Goals

• Use 2D tools to perform 3D operations• Model global and local surface• Input: just plain strokes

Goals

• Use 2D tools to perform 3D operations• Model global and local surface• Input: just plain strokes• Output: triangle mesh

Outline

• Motivations• Previous Work• Tool Workflow• Reconstruction• Adaptive Sampling & Depth Inference• Tool Interface• Results

Previous Work

• Depth painting [Williams, 1990]

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Previous Work

• Gradient editing [van Overveld, 1996]

Previous Work

• Maya 6.0 Artisan [Alias, 2004]

Outline

• Motivations• Previous Work• Tool Workflow• Reconstruction• Adaptive Sampling & Depth Inference• Tool Interface• Results

Tool Workflow

• First step: drawing input– Displacement map

• mid-grey = 0• white > 0• black < 0

Model of 3D sphere

Pencil

Brush

Tool Workflow

• First step: drawing– Displacement map– 2D shape boundary

(in green)• defines drawing mask

Tool Workflow

• First step: drawing– Displacement map– 2D shape boundary– Displacement regions (from 2 maps)

Tool Workflow

• Second step: modeling– Displace existing vertices

Tool Workflow

• Second step: modeling– Displace existing vertices– Create new surface patch

Tool Workflow

• Changing viewpoint

Modeling by drawing

Changing viewpoint

Reconstruction

• Based on evolving pseudo-manifold [Chaine, 2003]

Reconstruction

• Based on evolving pseudo-manifold [Chaine, 2003]

• Satisfy our requirements– Arbitrary number of connected components

Reconstruction

• Based on evolving pseudo-manifold [Chaine, 2003]

• Satisfy our requirements– Arbitrary number of connected components– Handle points off shape boundary

Reconstruction

• Based on evolving pseudo-manifold [Chaine, 2003]

• Satisfy our requirements– Arbitrary number of connected components– Handle points off shape boundary– Interactive (5k points per second)

2D reconstruction

• Start: pseudo-curve lies on oriented edges of Delaunay triangulation

2D reconstruction

• During: pseudo-curve evolves as long as oriented Gabriel criterion is not met

2D reconstruction

• Stop: topologically consistent set of oriented edges

Sampling and Depth

• Adaptive sampling– Displacement map

• Pencil and brush datain color buffer

Color buffer

Sampling and Depth

• Adaptive sampling– Displacement map– Approximate disp. map

sampled at existing vertices

Sampling and Depth

• Adaptive sampling– Displacement map (D)– Vertex-Sampled disp.

map (V)– Error map

E = 1 – ABS(D – V)– Arbitrary error value

Sampling and Depth

• Adaptive sampling– Displacement map– Approximate disp. map– Error map– Sampling [Alliez, 2002]

Sampling and Depth

• Adaptive sampling• Depth inference

– Identify surface vertices

Vertices ID buffer

Sampling and Depth

• Adaptive sampling• Depth inference

– Identify surface vertices– Assign depth values

Depth buffer

Sampling and Depth

• Adaptive sampling• Depth inference

– Identify surface vertices– Assign depth values– Infer depth values

• from existing surface• by depth propagation

Outline

• Motivations• Previous Work• Tool Workflow• Reconstruction• Adaptive Sampling & Depth Inference• Tool Interface• Results

Tool Interface

• Hole marks– Comic books production

Hole marks

Stone #3 (Avalon Studios)

Tool Interface

• Hole marks– Comic books production– Our system

Hole mark

Tool Interface

• Video: Basic interface

Tool Interface

• Blobbing

Drawing White shadingDistance field Height field Surface

Tool Interface

• Depth modes (chosen by menu)

Modeling “at depth”Depth inference Frisket mode

Video

• Modeling a tree

Paper sketch 3D model obtained with Relief

Outline

• Motivations• Previous Work• Tool Workflow• Reconstruction• Adaptive Sampling & Depth Inference• Tool Interface• Results

Results

• Models (1k to 4k points)

Discussion

• Intuitive shading convention

Discussion

• Intuitive shading convention• Problems with drawing metaphor

– No continuous visual feedback• Provide two modes

Discussion

• Intuitive shading convention• Problems with drawing metaphor

– No continuous visual feedback– Difficult to obtain continuous shading

• Provide higher-level drawing tools

Conclusion

• Modeling by drawing, but imprecise

Conclusion

• Modeling by drawing, but imprecise• Future work

– Speedup with local 3D reconstruction

Conclusion

• Modeling by drawing, but imprecise• Future work

– Speedup with local 3D reconstruction– Improve depth inference

Conclusion

• Modeling by drawing, but imprecise• Future work

– Speedup with local 3D reconstruction– Improve depth inference– Image-space and object-space sampling

Acknowledgements

This work has been performed while the first author was a visiting research fellow at Princeton University, supported by an INRIA post-doctoral fellowship.

Many people have indirectly contributed to it. We would like to thank: Adam Finkelstein, Szymon Rusinkiewicz, Jason Lawrence, Pierre Alliez, Mariette Yvinec, Laurence Boissieux, Laure Heïgéas, Laks Raghupathi, Olivier Cuisenaire, Bingfeng Zhou.