Interactive Point-based Modeling of Complex Objects from Images

Pierre Poulin (a,b)Marc Stamminger (a,c)François Duranleau (b)

Marie-Claude Frasson (a)George Drettakis (a)

(a) REVES, INRIA Sophia Antipolis(b) DIRO, Université de Montréal

(c) University of Erlangen

Modeling Complex Objects

Modeling Complex Objects

• High visual complexity

• Time consuming

• Algorithms for specialized objects– e.g., plants, mountains, etc.

• Adaptive rendering

• Many applications need such objects

Key Observations

• Extracting complex models from photos is a very powerful approach

• Point-based representation is very effective for complex models– Efficient display and storage

• User interaction is beneficial when extracting quality models– Specify where details are needed

– Resolve some ambiguities

Image-based Point Modeling

• Images are very flexible– Reality-based (photos)– Acquisition is easy

Image-based Point Modeling

• Points are very flexible– Fast rendering (hardware support)– Adaptive rendering for interactive display

Stamminger

Image-based Point Modeling

• Points are very flexible– Hierarchical organization and levels of detail

Q-splat

Image-based Point Modeling

• Points are very flexible– Visual quality– Many recent advances

Deussen

Automatic ReconstructionImages

ReconstructionProcess

Constraints

3D Model

Image

Interactive ReconstructionImages

ReconstructionProcess

Constraints

3D Model

Image

User

new imagesrequantizerecalibrate

Interactive ReconstructionImages

ReconstructionProcess

Constraints

3D Model

Image

User

color comparisonsplausibility threshold

new depth mapszone of interest

Interactive ReconstructionImages

ReconstructionProcess

Constraints

3D Model

Image

Userrevalidate the pointsrequest more pointsdecimate the points

jitter the pointssample with patterns

hole filling

Interactive ReconstructionImages

ReconstructionProcess

Constraints

3D Model

Image

User

undo changesremove pointsadd polygons

Interactive Reconstruction

• Interactive display– 6 M points/sec. on a PIII 1GHz with GeForce3

• Efficient reconstruction algorithm– Test more than 1K points/sec.

• Simple and intuitive controls– Direct interaction with the points

Computer Vision Contributions

• 3D scanners

• Structured light

• Stereo – N-views

• Shape-from-X

• Volumetric

Volumetric Reconstruction

• Voxel coloring and Space carving– If a voxel is impossible, carved out of object– Silhouettes, transparency, shading– Photo-consistency

SeitzKutulakos

Image-based Polygon Modeling

• Academic: Façade, Rekon, Reality

• Industry: RealViz, Canoma, Photomodeler

Façade

Image-based Polygon Modeling

• Small polygonal scene (30-100 polygons)

• Extracted textures and illumination

Boivin

Input Images (4/14)

Input Images

• Digital camera: Canon EOS-DS30

• 1080x720 and 2166x1440

• Fixed aperture and shutter speed

• Try not to change zoom

• OpenGL and ray traced test scenes

Camera Calibration

Camera Calibration

• ImageModeler from RealViz

• Fiduciary marks placed around the object

• Interactive system

• Intrinsic and extrinsic camera parameters

3D Zone of Interest

Initial Random Points

Initial Random Points

• Generated randomly within the envelope

• More specific patterns discussed later

• Projection of a point in each photo

• Gather colors

Color Comparison

• Euclidean distance– RGB, CIE xy, CIE Luv, CIE Lab

– Speed vs. accuracy

• Color quantized images– Precomputed (ppmquantall or more sophisticated)

– Quantization only on projected zone of interest

– 32 to 128 colors

– Reduce shading variations

– Efficient test for color equality

C: 25%B: 50%

Dominant Color

A: 100%

Plausibility

100%33%

with visibility

Random Points with Depth Maps

Depth Maps

• Computed from the current set of points

• Updated on user demand

• With depth maps, can raise the plausibility threshold

• Generate more points within the object

• Re-evaluation of previously generated points

Clean-up Points

Clean-up Points

• In general– Increase color threshold and re-evaluate

• With good depth maps– Project in each image– Reject if point visible and color too different

Generate More Points

Generate More Points

• Randomly

• Stratified sampling based on voxels

• Point decimation based on voxels

Guide the Points

Guide the Points

• Smaller 3D sphere of interest– Generate more points– Eliminate all points

• 3D flood fill for branching patterns

• Patterns for planar surfaces

• Patterns for boundary surfaces

Filling with no Leaves

Filling with Leaves

Jitter the Points

Reprojection

Stepping through it again

Results

Scene Images Resolution Colors Points

Fruit bowl 13 512x512 - -

Soldier 13 2160x1440 64 118K

Snack 8 1440x960 64 120K

Ficcus 13 2160x1440 64 150K

Synthetic Fruit Bowl

ray tracingcolor points reprojection

Toy Soldier

color pointscolor points reprojection

Snack

Snack

Ficcus

Conclusions

• Point-based reconstruction of complex objects from images

• Tight integration– 3D color point representation– User-driven and/or automatic reconstructions– Interactive display

• Flexible to integrate most advances in computer vision

Findings

• First steps are encouraging, but objects are still of limited realism

• Information in photos is inspiring, but also difficult to analyse correctly

• How many things in a pixel?

• How many pixels and colors for an object?

Future Work

• Video sequences

• High dynamic range photos

• Shadows and shading in color comparison

• Extraction of limited BRDFs

• 3D texture synthesis of materials

Questions

• Did you…

• Is it…

• Can you…

• When…

• What…

• Where…

User Interaction in Modeling

• Specify regions of interest, thresholds, validity

• Control the visual quality

• Iterative refining process

• Guide the solution

• Automatic or interactive process

• Interactive display (6 M points/sec. GeForce3)

Image-based Point Modeling

• Difficulties with points – Visibility

• Holes in surfaces, size of points

• Filtering the representation and the texture

• Not our goal to fix these difficulties for now

LOD in Graphics

• Environment maps

• Billboards

• Textured polygons

• Layer-depth images

• Light field / lumigraph

3D Scanners

• Very good results in general

• Size of the scanner wrt object

• Costs

• Fixed illumination

Stereo - N views

• Camera calibration• Epipolar constraints• Color matching• 3D position and color• Difficulties

– Holes and occlusions– Sharp edges, noise, shading

• Infinity of shapes in general• Targeted for object recognition and collision avoidance• Only recently goal of photo-realism

Shape-from-X

• Silhouettes

• Shadows

• Focus/defocus

• Motion

• Shading

• etc.