Who is using Open3DGC?

Please, let me know if you are using open3dgc in your products/projects/articles

• Khronous glTF
• SEA3D Studio
• Cesium
• BLAST

Latest V-HACD source code

The latest version of the V-HACD code is available here https://github.com/kmammou/v-hacd

A Simple C++ Class for 3D Mesh Decimation

Some time ago I wrote a simple C++ class to simplify a 3D mesh. It is based on Michael Garland's article "Surface Simplification Using Quadric Error Metrics"

The code is available under a BSD license.

A win64 executable is also available for quick testing.

The code produces good quality results. However, it is relatively slow!

Usage:
MeshSimplification fileNameIn.obj targetNTrianglesDecimatedMesh targetNVerticesDecimatedMesh maxDecimationError fileNameOut.obj

For instance, to generate a decimated mesh with 1000 triangles use the following command line:
MeshSimplification input.obj 1000 0 1.0 decimated.obj

To generate a decimated mesh with 1000 vertices:
MeshSimplification input.obj 0 1000 1.0 decimated.obj

To generate a decimated mesh with an error of 1%:
MeshSimplification input.obj 0 0 0.01 decimated.obj

To use the mesh decimation class in your code, proceed as follows:

// input mesh    
Vec3<Float> * points; 
Vec3<int> * triangles;

// Fill points and triangles with input mesh
// ...

// decimate mesh
MeshDecimator myMDecimator;
myMDecimator.Initialize(nPoints, nTriangles, points, triangles);
myMDecimator.Decimate(targetNVerticesDecimatedMesh, targetNTrianglesDecimatedMesh, maxDecimationError);

// allocate memory for decimated mesh
size_t nVerticesDecimatedMesh = myMDecimator.GetNVertices();
size_t nTrianglesDecimatedMesh = myMDecimator.GetNVertices();
Vec3<Float> * decimatedMeshPoints = new Vec3<Float>[nVerticesDecimatedMesh];
Vec3<int> * decimatedMeshTriangles = new Vec3<int>[nTrianglesDecimatedMesh];

// retrieve decimated mesh
myMDecimator.GetMeshData(decimatedMeshPoints, decimatedMeshTriangles);

Using V-HACD for 2D Approximate Convex Decomposition

Unreal Engine 4 switches to V-HACD

OpenCL Acceleration for V-HACD

Since I don’t expect to have time in the near future to work on V-HACD , I am writing this post to keep track of the performance improvements obtained after the latest optimizations I added. The source code is available here.

Experimental Evaluation
Table 1 compares the computation times (cf. Section "Machine description") of V-HACD 2.0 and V-HACD 2.2 obtained by using the configuration described below (cf. Table 2). These results show that V-HACD 2.2 is an order of magnitude faster than V-HACD 2.0. The gains are mainly obtained thanks to the convex-hull approximation (cf. Section "Updates"). The OpenCL acceleration provides 30-50% lower computation times when compared to the CPU-only version of V-HACD 2.2. 

The code is still not fully optimized and more improvements could be expected!

	V-HACD 2.0 (CPU only)	V-HACD 2.2 (CPU+GPU)
army_man	650s	65s
block	220s	26s
Bunny	317s	30s
Camel	388s	34s
Casting	744s	79s
Chair	408s	42s
Cow1	314s	30s
Cow2	349s	32s
deer_bound	411s	34s

Table 1: Computation times: V-HACD 2.0 vs V-HACD 2.2 

Parameter	Config. 1
resolution	8000000
max. depth	20
max. concavity	0.001
plane down-sampling	4
convex-hull down-sampling	4
alpha	0.05
beta	0.05
gamma	0.0005
delta	0.05
pca	0
mode	0
max. vertices per convex-hull	64
min. volume to add vertices to convex-hulls	0.0001
convex-hull approximation	1

Table 2: Tested configuration 

Updates

V-HACD 2.2 include the following updates:

1. OpenCL acceleration to compute the clipped volumes on the GPU
2. Convex-hull approximation to accelerate concavity calculations
3. Added local concavity measure to clipping cost calculation
4. Changed command line parameters

To do
When I'll find time (probably not soon), I need to do the following:

• Test the code and build executables for different platforms (i.e., Linux, Mac OS), and 
• Update the Blender add-on to make it work with the new command line parameters.

Machine description

• OS: Windows 8.1 Pro 64-bit
• CPU: Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz (8 CPUs), ~3.4GHz
• GPU: NVIDIA GeForce GTX 550 Ti
• Memory: 10240MB RAM

[V-HACD] Adaptive Convex-Hulls Sub-sampling

Today, I took some time to add a new parameter (i.e., minVolumePerCH) to V-HACD to adaptively control the number of vertices/triangles of the generated convex-hulls. Below some results for different values of minVolumePerCH.

Who is using V-HACD?

Please, let me know if you are using V-HACD in your products

• Leadwerks
• 3DEXCITE
• Unreal Engine 4
• DBPro
• HighFidelity
• Bonzai Engine

V-HACD Blender Add-on (by Alain Ducharme)

Checkout this Blender add-on released by Alain Ducharme!

V-HACD 2.0 Parameters Description

Parameter name	Description	Default value	Range
resolution	maximum number of voxels generated during the voxelization stage	100,000	10,000-64,000,000
depth	maximum number of clipping stages. During each split stage, all the model parts (with a concavity higher than the user defined threshold) are clipped according the "best" clipping plane	20	1-32
concavity	maximum concavity	0.0025	0.0-1.0
planeDownsampling	controls the granularity of the search for the "best" clipping plane	4	1-16
convexhullDownsampling	controls the precision of the convex-hull generation process during the clipping plane selection stage	4	1-16
alpha	controls the bias toward clipping along symmetry planes	0.05	0.0-1.0
beta	controls the bias toward clipping along revolution axes	0.05	0.0-1.0
gamma	maximum allowed concavity during the merge stage	0.00125	0.0-1.0
pca	enable/disable normalizing the mesh before applying the convex decomposition	0	0-1
mode	0: voxel-based approximate convex decomposition, 1: tetrahedron-based approximate convex decomposition	0	0-1
maxNumVerticesPerCH	controls the maximum number of triangles per convex-hull	64	4-1024
minVolumePerCH	controls the adaptive sampling of the generated convex-hulls	0.0001	0.0-0.01

V-HACD 2.0 vs. HACD

Below some approximate convex decomposition results comparing V-HACD 2.0 and HACD.

Parameters:

• testVHACD.exe %%i 8000000 20 0.003 4 4 0.05 0.05 0.0015 0 0 256 0.0 %%i.wrl log_%%i.txt
• testHACD.exe %%i  2 50 0 0 1 30 2000

Score:

• +1: V-HACD provides better decomposition than HACD
• -1: HACD provides better decomposition than V-HACD
• 0: V-HACD and HACD provide comparable results

V-HACD 2.0	HACD	Score
18 convex-hulls	66 convex-hulls	1
18 convex-hulls	26 convex-hulls	1
16 convex-hulls	12 convex-hulls	-1
30 convex-hulls	28 convex-hulls	0
46 convex-hulls	54 convex-hulls	1
18 convex-hulls	26 convex-hulls	1
16 convex-hulls	19 convex-hulls	0
18 convex-hulls	22 convex-hulls	0
34 convex-hulls	17 convex-hulls	1
18 convex-hulls	13 convex-hulls	-1
25 convex-hulls	28 convex-hulls	1
20 convex-hulls	15 convex-hulls	-1
22 convex-hulls	16 convex-hulls	-1
42 convex-hulls	42 convex-hulls	0
9 convex-hulls	7 convex-hulls	-1
16 convex-hulls	15 convex-hulls	1
35 convex-hulls	37 convex-hulls	0
17 convex-hulls	17 convex-hulls	0
18 convex-hulls	18 convex-hulls	0
36 convex-hulls	34 convex-hulls	0
13 convex-hulls	10 convex-hulls	-1
11 convex-hulls	5 convex-hulls	-1
24 convex-hulls	20 convex-hulls	1
35 convex-hulls	36 convex-hulls	0
21 convex-hulls	13 convex-hulls	-1
15 convex-hulls	16 convex-hulls	0
9 convex-hulls	10 convex-hulls	0
41 convex-hulls	71 convex-hulls	0
22 convex-hulls	27 convex-hulls	0
47 convex-hulls	51 convex-hulls	0
23 convex-hulls	28 convex-hulls	0