Compression of a three-dimensional modeled object

Patent metadata

Field	Value
Publication number	US-9972129-B2
Application number	US-201615339676-A
Country	US
Kind code	B2
Filing date	Oct 31, 2016
Priority date	Oct 30, 2015
Publication date	May 15, 2018
Grant date	May 15, 2018

Abstract

The invention notably relates to a computer-implemented method for compressing a three-dimensional modeled object. The method comprises providing a mesh of a three-dimensional modeled object, parameterizing (u,v) the mesh on a two-dimensional plane, converting the parameterized mesh into an image I, defining a grid of cells from the image I, each cell being defined by a set of control points, computing a relevance of each cell of the grid, determining at least one cell having a relevance lower than a pre-determined threshold, resizing the at least one determined cell and computing a resulting grid, computing a transformation for each pixel of the image from the resulting grid, and applying the computed transformation to the image I.

First claim

The invention claimed is:

1. A computer-implemented method for compressing a three-dimensional modeled object, comprising:
providing a mesh of a three-dimensional modeled object;
parameterizing (u,v) the mesh on a two-dimensional plane;
converting the parameterized mesh into an image I;
defining a grid of cells from the image I, each cell being defined by a set of control points;
computing a relevance of each cell of the grid;
determining at least one cell having a relevance lower than a pre-determined threshold;
resizing the at least one determined cell and computing a resulting grid;
computing a transformation for each pixel of the image from the resulting grid; and
applying the computed transformation to the image I.

2. The computer-implemented method of claim 1 , wherein the determination of the said at least one cell having a relevance lower than a pre-determined threshold comprises the determination of a set of one or more cells having the lowest relevance.

3. The computer-implemented method of claim 1 , further comprising:
providing an image significance by computing a significance of each pixel in the image I;
extracting an original spatial domain (Ω) of the Significance Image;
providing a transformation T θ , parameterized as an interpolating spline by a set of control points, from the original spatial domain (Ω) to a resized spatial domain (Ω′),
wherein defining a grid of cells from the image I comprises subdividing the original spatial domain (Ω) into cells, each cell being defined by a subset of control points of the set,
wherein computing a relevance of each cell of the grid comprises computing, for each cell, a weighted average of the significance of the pixels in the cell, the weighted average being computed using the interpolating spline, and
wherein computing a transformation for each pixel of the image I from the resulting grid comprises computing the transformation T θ over a spatial domain of the image I obtained by the conversion of the parameterized mesh from the resulting grid.

4. The computer-implemented method of claim 3 , wherein the transformation T θ is parameterized as a free form deformation interpolating spline,
wherein displacement function u θ (x) is defined by the equation

u
θ

⁡

(
x
)

=

∑

k
=
0

3

⁢

⁢

∑

l
=
0

3

⁢

⁢

B
k

⁡

(

x
w

s
w

-

⌊

x
w

s
w

⌋

)

⁢

B
l

⁡

(

x
h

s
h

-

⌊

x
h

s
h

⌋

)

⁢

θ
⁡

(

x
w

s
w

+
k

,

x
h

s
h

+
l

)

wherein B 0 , B 1 , B 2 , B 3 are Cubic B-splines functions, θ is a function representing the control points, s w is the spacing between two control points in an horizontal direction, s h is the spacing between two control points in a vertical direction, x w is the position of a pixel in the original spatial domain (Ω) on an horizontal axis, x h is the position of a pixel in the original spatial domain (Ω) on a vertical axis, and
wherein the set of control points form a grid and θ is the function representing the grid of control points.

5. The computer-implemented method of claim 3 , wherein the computation of the significance of each pixel is carried out with the function
SI=αE geom +(1−α) E norm

where α ϵ [0;1],
where

E
geom

=

∑

c
∈

Assignees

• Dassault Systemes

Inventors

• Michel Fabrice
• Boulkenafed Malika

Classifications

• G06T9/00 Primary

  Image coding (bandwidth or redundancy reduction for static pictures H04N1/41; coding or decoding of static colour picture signals H04N1/64; methods or arrangements for coding, decoding, compressing or decompressing digital video signals H04N19/00) · CPC title

• G06T17/20 Primary

  Finite element generation, e.g. wire-frame surface description, {tesselation} · CPC title

• G06K9/4671

  Physics · mapped topic

• G06T3/40

  Scaling of whole images or parts thereof, e.g. expanding or contracting · CPC title

• G06K9/4652

  Physics · mapped topic