Evaluation of Stereo Vision Algorithms

Grant Latham

Self-Driving Car Group

http://www.sdc-csm.com/

ehildenb@mines.edu

Outline

Stereo Vision – Overview

Popular Algorithms

Hardware

Evaluation Data Sets

Process and Results

Continuing Work and Recommendations

Stereo Vision - Overview

Motiviated by human binocular depth perception – leveraging difference in angle between two eyes.

Stereo Vision - Overview

Image from Dr. William Hoff

Stereo Vision - Algorithms

Challenge: finding matching pixels

Most common Algorithms:

Block Matching (BM)

Semi-Global Matching (SGM)

Hardware - Cameras

See3CAM_10CUG from e-con Systems

1.3 MegaPixel

Global shutter with external trigger

Interchangeable Lenses

USB 3.0/2.0 compatible

Hardware - GPU

NVidia Jetson TK1 embedded system

Kepler GPU with 192 CUDA cores

Supported by some OpenCV functions

Evaluation Datasets

Image pairs with “ground truth” found by pattern projection

Available from Middlebury: http://vision.middlebury.edu/stereo/data/scenes2014/

Process

Tested OpenCV’s basic Block Matching algorithm on PC CPU and Nvidia GPU;

GPU reduced processing time by ≈75%

Process

Further tests performed on CPU for convenience

Disparity images obtained from OpenCV’s BM and SGBM algorithms

Results exported to Matlab for analysis

Results – Sample Image

BM Time (s)

SGBM Time (s)

BM Total Error

SGBM Total Error

BM Partial Error

SGBM Partial Error

BM Avg Error

SGBM Avg Error

2.4 68 0.52 0.19 0.05 0.07 1.71 4.35

Ground Truth Block Matching Semi-Global Block Matching

Results - Complete Image Name

BM Time (s)

SGMB Time (s)

BM Total Error

SGBM Total Error

BM Partial Error

SGBM Partial Error

BM Avg Error

SGBM Avg Error

Adirondack 2.1 65 0.74 0.39 0.07 0.07 1.01 2.24

Backpack 2.4 68 0.52 0.19 0.05 0.07 1.72 4.35

Jadeplant 2.0 59 0.97 0.85 0.31 0.40 -1.35 -19.90

Motorcycle 2.6 68 0.50 0.19 0.04 0.05 1.37 3.17

Piano 2.4 62 0.62 0.26 0.07 0.10 1.47 5.14

Pipes 2.6 65 0.57 0.22 0.08 0.09 1.81 4.76

Playroom 2.1 60 0.77 0.53 0.12 0.13 1.96 3.79

Playtable 2.1 57 0.63 0.26 0.04 0.05 0.99 2.32

Recycle 2.3 63 0.86 0.35 0.12 0.05 0.97 1.83

Shelves 2.5 67 0.72 0.42 0.11 0.22 1.27 8.83

Vintage 2.3 63 0.91 0.70 0.09 0.19 -0.30 -9.32

Recommendations

GPU Implementation

Basic BM algorithm for speed, conditional accuracy

Continuing Work

Utilize traffic scene priors

Schneider, N.; Franke, U., "Exploiting Traffic Scene Disparity Statistics for Stereo Vision," Computer Vision and

Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on , vol., no., pp.688,695, 23-28 June 2014

Utilize HSV domain

Lazaros Nalpantidis, Antonios Gasteratos, "Stereo vision for robotic applications in the presence of non-ideal

lighting conditions", Image and Vision Computing, Volume 28, Issue 6, June 2010

EL FIN

(Questions)