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Full Body Scanning by Daniel Evora. Calibration Left & Right.

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Presentation on theme: "Full Body Scanning by Daniel Evora. Calibration Left & Right."— Presentation transcript:

1 Full Body Scanning by Daniel Evora

2 Calibration

3 Left & Right

4 Mesh 101158 triangles


6 Extracting 2D Points from the images  Decode Construct the 3D Points  Triangulation

7 Create Mesh  Getting Rid of Long edges / Far away Neighbors Filling Holes and Mesh Smoothing

8 Mesh Alignment  Combining Meshes into Final Model  Poisson Surface Reconstruction Software

9 Josh Tutwiler Goal: to construct a 3-D model of a bowling pin from 2-D images.

10 Smooth the Mesh nbr_smooth –Move each point to the mean of its neighbors

11 Meshes



14 Computer Vision Default Project by Patrick Flynn Original Image

15 Computer Vision Default Project Image Scans – 3 viewpoints

16 Computer Vision Default Project Manually Cleaned Up

17 Computer Vision Default Project Aligned with my ICP (lsqnonlin)

18 Default Project by Phong Vuong Right Left

19 Mesh Cleaning

20 Mesh Alignment

21 Poisson Surface Reconstruction

22 Default Project by Roula Lagaditis Chosen Object: Bender-bot Using structured light, recovered front, back, right and left

23 Default Project Chosen Object: Bender-bot Mesh Aligning - Front and Back scanned images, using “rigid-alignment”

24 Default Project Chosen Object: Bender-bot Final recovered shape

25 CS117 Final Project Danny Miller 3 objects ~ 7 scans per object ~ 2 GB of pictures Idea – adding a green backdrop could make it easy to filter out the background Green tablecloth from Party City - 79¢


27 Green Removal Created a windows program in C# to remove the green from pictures.

28 Problems - Reflectivity

29 Scans 5 stage scanning process –Auto-Pruning Several passes –User Pruning 3 views in 2d 30 views in 2d –Smoothing Several passes –Normals –Colors

30 Alignment Using the linear algebra approach

31 Color?

32 Mesh Creation


34 Object Centric Photo Browsing Tony Tran Input

35 Part3: Estimating relationship between images. Image i’s sift pointsImage j’s.sift points Input: Find Correspondences (matches) Compute Essential matrix and remove outlier matches RANSAC E Remove incoherent matches based on a triangulation heuristic Remove bad matches With triangulation heuristic

36 CS 117 Project: Motion Capture By Blake Atkinson Materials 5 different colored sets of appx. 3v LEDs Electrical tape Glove 9v Batteries Red and Black Wire More patience than you have

37 Since the epipolar lines are calculated using the Fundamental Matrix, which is calculated from your initial SIFT points, they too should land on the epipolar lines. If not you’ve done something wrong. Here we have the SIFT points (red & yellow) and the corresponding epipolar lines (blue) based on those points. The left image gets it’s epipolar equations from the right points, and vice versa. Epipolar Geometry by Nick Schiffelbein

38 Automating Camera Calibration Sam Hallman But how do you solve for a matrix??

39 Try #2 with the MK symbol

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