R I T Rochester Institute of Technology Geometric Scene Reconstruction Using 3-D Point Cloud Data Research Plan Feng Li and Steve Lach Advanced Digital

R I T Rochester Institute of Technology

Geometric Scene Geometric Scene Reconstruction Using 3-D Reconstruction Using 3-D

Point Cloud DataPoint Cloud Data

Research Plan

Feng Li and Steve Lach

Advanced Digital Image Processing: SIMG786

April 6, 2006

R I T Rochester Institute of Technology2

OverviewOverview

•LIDAR Basics and Terminology

•Purpose/Overview

•Research Overview

•Approach

•Schedule

•Summary


LIDAR BasicsLIDAR Basics

•LIDAR: LIght Detection And Ranging

•LIDAR works like a radar system except

it uses light instead of radio frequencies GPS: measure LIDAR sensor in the air

IMU (Inertial Measuring Unit): measure the roll, pitch and heading of the aircraft

LIDAR sensor: measure angular orientation of laser pulse; measure time-interval between light reflects off the object and returns to the sensor

www.ncfloodmaps.com


LIDAR Basics - ContinuedLIDAR Basics - Continued

• Time of flight system produces 3D spatial

imagery

– Range resolution: ability to resolve two separate objects in

depth

– More sophisticated techniques discussed elsewhere

– First return, interpolated DEM (range-based) is most

common data set produced by commercial vendors

• Intensity image

yields additional

information281989.740360 4773791.856553 134.944699 187

281990.740358 4773791.854912 134.971924 187

281991.740357 4773791.853271 135.253379 187

281992.740356 4773791.851630 136.049035 187

281993.740354 4773791.849989 136.452911 187


Some Additional TerminologySome Additional Terminology

Our Terminology

• Point Cloud – Set of irregularly spaced 3D points

• Range Image - 3D points at regular locations on an x, y grid

• Digital Elevation Model (DEM)/Digital Surface Model (DSM) – Blanket over scene

• Digital Terrain Model (DTM) – Bare Earth (remove trees, buildings, etc)

• Voids – Portions of a DEM where no elevation data is available

Image courtesy of Ma, “BUILDING MODEL RECONSTRUCTION FROM LIDAR DATA AND AERIAL PHOTOGRAPHS”, PhD

Dissertation, OSU, 2004.


Underlying ProblemUnderlying Problem

•Through courses, we have learned to

process regularly-sampled images

•However, we have a much smaller toolset

for processing point cloud data

•We will develop tools for this new “image”

type, and apply to data from RIT LIDAR

collect


Three 3D Working EnvironmentsThree 3D Working Environments

• Range Image (Interpolate irregularly distributed pointsto a regular grid)

– Traditional, Somewhat Simple

– Cost: loss information or lead to biases

(Mixture of points from different categories)

• Point Cloud– Accurate

– Difficult to Process; Can not use standard image processing routines

(median filter, FFT)

• Combination of the Two– Use Range Image to classify regions

– Use Point Cloud to produce accurate results


Purpose of ProjectPurpose of Project

• Use 3-D Point Data to geometrically reconstruct a scene

• Goal: Determine Appropriate Ways to:

1. Efficiently handle the data (relationship between the points) 2. Operate on the data (filter, interpolate, etc) 3. Extract objects (buildings and trees) 4. Reconstruct objects (buildings and trees) Focus will primarily be on Item #4, Other items will be done as required to support this task (additional work if schedule permits)


Research OverviewResearch Overview

Identify GroundPoints

Create GeometricTerrain Model

Classify Non-Ground points

Create SpectralTerrain Model

Overlay SpectralTextures

Create GeometricObject Models

Learn to Process3D Data


Approach: Goal #1 (Data Approach: Goal #1 (Data Handling)Handling)

•Use range image to isolate groupings of

points belonging to a single object

•Pixelize or Voxelize the data for each object

(unsure which is preferable)

•Add extra data fields (i.e. X, Y, Z, I, Obj,

Vox, Sub Vox, Neighbors, Distances,

Directions)

•Initial Processing Likely to be

computationally intense


ApproachApproach: Goal #2 (Point Cloud : Goal #2 (Point Cloud Filtering)Filtering)

•Use sliding window approaches – functional

relationships based on distances, not

discrete kernel

•Anticipate creating Median, Mean (low-

pass), Weighted Mean, Differencing (high-

pass)


ApproachApproach: Goal #3 (Object : Goal #3 (Object Extraction)Extraction)

3 Steps in achieving this goal:

• First Step: Generate range image - may need to

work on interpolation techniques, right now will

use pre-packaged routine


ApproachApproach: Goal #3 (Object : Goal #3 (Object Extraction)Extraction)

• Second Step: Use range image to generate DTM via

Median Filtering and High-Passs Filtering


Generating DTMGenerating DTM

LIDAR Point DataInitial

Data Filtering

Remove Non-ground pixels

FinalData Filtering

• Thresholding above Global Estimated Ground Polynomial

• Thresholding above Local Estimated Ground Polynomial

• Threshold along rows/columns

• Modified Median/Thresholding

• High-Pass Filtering (Gradient, Laplacian)

• Nearest Neighbor, Triangular, Bilinear…

• Weighted value techniques based on Delaunay triangulation, Natural Neighbor, etc…

• Kriging

Identify Non-ground pixels

Interpolate to Grid

Interpolate AcrossRemoved Points


Result of Median FilterResult of Median Filter

Baseline Range Image (2m Centers)

Flagged Points with Modified Median Filter (Center 10m,

Outer 40m)


Result of Median Filter Plus HP Result of Median Filter Plus HP FilterFilter

Baseline Range Image Flagged Points with Modified Median Filter and High Pass

Filter


High Points RemovedHigh Points Removed

Baseline Range Image Scatter Plot with Flagged Points Removed


Interpolation and Low-Pass Filtering Interpolation and Low-Pass Filtering (Final DTM)(Final DTM)

Baseline Range Image (2m Centers)

Final Terrain Model after re-interpolation and

smoothing


Object Extraction: Segmenting Buildings Object Extraction: Segmenting Buildings and Treesand Trees

Building/Tree Map using only morphological techniques

• Third Step: Differentiate High Points

• Can continue to work with range image, or go back to Point Cloud

• A host of features available:

• Length of edges

• Homogeneity of height (HP Filtering)

• Plane matching

• Morphological Techniques

• Exploitation of co-registered Spectral Image


ApproachApproach: Goal #4 (Object : Goal #4 (Object Reconstruction)Reconstruction)

Trees

• Use blurred Lidar Height Information

• Find local maxima to identify potential tree centers

• Use “Circle” functions at various scales to isolate features with high radial symmetry (Correlation Technique); confirm tree location and determine spread

• Use results to pick tree from library of objects

• This technique will also help extract cylindrical features for use in building reconstruction

Tree Extraction Algorithm

* Ref Gray et al: “Scene Construction Methodologies and Techniques for Simulating Forest Areas”, 11th Annual Ground Targets Modeling and Validation Conference, 2000.


ApproachApproach: Goal #4 (Object : Goal #4 (Object Reconstruction)Reconstruction)

Buildings

• Method 1: Use range image and corner detector to find critical vertices (fairly simple)

• Method 2: Planar Patch Extraction, find edges via plane intersections (More Accurate) – Anticipate significant effort here (Ref: Schenk, “Detecting Planes by Hough Transform”)

• Method 3: Rectangular (and 3D Primatives) Reconstruction:

Images Courtesy of Morgan and Habib, “Interpolation of Lidar Data and Automatic Building Extraction”, ASPRS Annual Conf,

2002

and

Haala, “Laser Data for Virtual Landscape Generation”, IAPRS, Vol 32, 1999


Planar Feature Planar Feature Extraction/ReconstructionExtraction/Reconstruction

• Several techniques can be used to detect planar determine

parameters

– Segment images of roof gradient directions

– Hough-based techniques

– Clustering (based on the meshes of a Delaunay triangulation) – we will explore this

first


ScheduleSchedule

Literature Search

Data Handling

Operating on Data

Extracting Objects

Simple Processing on Range Image

Use of Height Features for Segmentation

Reconstructing Objects

Range Image Techniques

Planar Extraction Techniques

Rectangular Fitting Technique

Reports

4/2 4/9 4/16 4/23 4/30 5/7 5/14 5/21


SummarySummary

• We will be using 3-D Point Data to

geometrically reconstruct a desired scene

• Basic technique is to use range image to

isolate points, then process point cloud to

do the reconstruction

• Focus on feature extraction rather than

point analysis

Documents

R I T Rochester Institute of Technology Geometric Scene Reconstruction Using 3-D Point Cloud Data Research Plan Feng Li and Steve Lach Advanced Digital