Research Objective(s)

Q: can we develop a cost effective workflow for capturing products.

• Textures quality is critical

• Wide range of object:
  • Shapes
  • Sizes
  • Reflectance

Questions:

• Are there open source solutions that we can use commercially?
• Are open source solutions mature enough to use?
• Can a fixed time investment to capture session ensure success?  
• What Type of Capture rigs might we have to design/build?
• How much compute (time) is required to process 
• What operator skills are required

Next:  What Technologies?

OPTIONS for Reality Capture - RGB-D (IR)

• ($$) Kinect
• ($$) Structure (Occipital)
• ($$$$$$) ArcTec EVA 
• ($$) Tango (tablet/phone) *

*If you have a tango device, i would like to chat with you.

OPTIONS FOR REALITY CAPTURE - SL/Laser

• ($$$$-$$$$$) David Laser
• ($$) Eora-3d
• ($) DIY Laser

OPTIONS FOR REALITY CAPTURE - Photogrammetry

Commercial*

• AutoDesk ReCap/Memento/ReMake
• Agisoft PhotoScan
• Capturing Reality(.com)
• Pix4D

Open Source*

• VisualSFM /CMMS + MeshLab
• VisualSFM/OpenMVS

*For an appropriately comprehensive list:

https://en.wikipedia.org/wiki/Comparison_of_photogrammetry_software

The focus of this talk

The chief objective of a photogrammetric process is to determine the distance between points in 3d space.

Simple Right?

Basics

• Point - simple a point in 3D space (x:0, y:0, z:0)
• Point Cloud - a collection of point in space, generally representing a multitude of points on a physical object.

Basics

Point clouds can have color, but they can't represent textures

Basics

In order to have textures, a point cloud but be converted to a mesh.  

• There are many  algorithms to convert clouds to meshes.
• It is difficult for a single algorithm to perfectly reconstruct any arbitrarily shaped point cloud.
• This a practical sense, this is the hardest (experimental) part of the process.

Basics

Structure from motion (SfM)

A range imaging technique; it refers to the process of estimating three-dimensional structures from two-dimensional image sequences which may be coupled with local motion signals.

tl;dr

Alogrithms + many pictures = form

A (simplified)  Workflow

1. Capture (The Object)
2. Feature Detection/Matching
3. Sparse Reconstruction
4. Dense Reconstruction
5. Mesh Reconstruction
6. Texturization
7. Export

2.

3.

4.

http://wedidstuff.heavyimage.com/index.php/2013/07/12/open-source-photogrammetry-workflow/

visualsfm + cmvs

meshlab

Experiment 1

Business Card Box

Experiment 2

Bicycle

Comparisons

Canon SX 230 HS (circa 2011 - 12MP)

Bicycle - 52 Image Capture

Autodesk Memento vs VisualSFM + Meshlab vs Agisoft PhotoScan

Takeaway:  Commerical Not always better

Walthrough

Business Card Box - 43 image capture

• Smoother Mesh
• Close to Manfold
• Rounded corner with highlight depressed.

• Flat Surface Missing points; causing rough mesh
• Open Mesh
• Handled rounded corner with highlight better.

Walthrough