R I T Rochester Institute of Technology Geometric Scene Reconstruction Using 3-D Point Cloud Data Feng Li and Steve Lach Advanced Digital Image Processing March 23, 2006

R I T Rochester Institute of Technology 2 Overview Research Goals Terminology/Introduction to LIDAR Data Interpolation DTM Extraction Building Reconstruction Summary

R I T Rochester Institute of Technology 3 Research Goals Use 3-D Point Data (LIDAR/IFSAR), possibly in combination with more traditional (passive) techniques to geometrically reconstruct a scene (terrain, buildings, trees, vegetation, etc) Determine Appropriate Ways to: - Efficiently handle the data - Operate on the data (filter, interpolate, etc) - Output the data in a useable format

R I T Rochester Institute of Technology 4 Some Basic Terminology My Terminology Point Cloud Digital Elevation Model (DEM) Digital Terrain Model (DTM) LADAR – Locates Position of an Object LIDAR – Derives the Properties of an Object Others Use Digital Surface Model (DSM) Digital Terrain Model (DEM) - ?!

R I T Rochester Institute of Technology 5 LIDAR Basics Fundamentally, LIDAR is a radar system which uses lasers as the active signal source Time of flight system produces 3D spatial imagery – Range resolution: ability to resolve two separate objects in depth – More sophisticated techniques discussed elsewhere (temporal gating, DIAL, Vibrometry, etc) – First return, interpolated DEM (range-based) is most common data set produced by commercial vendors Intensity image yields additional information

R I T Rochester Institute of Technology 6 Research Overview Ground is typically: Smoother Lower Potentially spectrally unique Identify Ground Points Create Geometric Terrain Model Classify Non-Ground points Interpolation across gaps Via: Overhead Shape Homogeneity of Height Vertical Edge Information Create Spectral Terrain Model Overlay Spectral Textures Create Geometric Object Models Using: Photogrammetry Tree modeling s/w From: Sensor data Spectral Library From: Sensor data Spectral Library + Systems Analysis

R I T Rochester Institute of Technology 7 Initial Work: Geometrical Terrain Modeling LIDAR Point Data Initial Data Filtering Remove Non- ground pixels Final Data 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 Across Removed Points

R I T Rochester Institute of Technology 8 Initial Work: Region of Interest

R I T Rochester Institute of Technology 9 Point Cloud Processing – Possible Technique… Voxelize the data (or Tree-type structuring) to determine spatial relationships among the points Pre-Filter Data to Remove Noise Identify Non-ground Points – Morphology Filters – Generate slowly varying surface in neighborhood, then threshold – Slope-Based Methods – Homogeneity of Height (or Intensity) Step through filtered data and compute surface normals at each point

R I T Rochester Institute of Technology 10 Interpolation Techniques A Per Intro to DIP : A H -1

R I T Rochester Institute of Technology 11 Interpolation Techniques A

R I T Rochester Institute of Technology 12 Result of Simple (Global) Thresholding Non-Ground Pixel Extraction

R I T Rochester Institute of Technology 13 Non-Ground Pixel Extraction via Polynomial Fits

R I T Rochester Institute of Technology 14 Non-Ground Pixel Extraction via Modified Median Thresholding 1. Find Median in small window 2. Flag Points > threshold (relative to median) within larger window

R I T Rochester Institute of Technology 15 Interpolated Height ImageSegmented Optical Image Non-Ground Pixel Extraction via Multi-Mode Techniques

R I T Rochester Institute of Technology 16 Interpolated Height Image Structure Uncovered Through Modified Median Filtering A Simple Example

R I T Rochester Institute of Technology 17 Interpolated Height Image Structure Uncovered Through Modified Median Filtering + High Pass Filtering A Simple Example

R I T Rochester Institute of Technology 18 Interpolated Height Image Point Cloud with Objects Removed A Simple Example

R I T Rochester Institute of Technology 19 Interpolated Height Image Final (Geometric) Terrain Model A Simple Example

R I T Rochester Institute of Technology 20 Summary I will be using 3-D Point Data in combination with more passive imagery to geometrically reconstruct a desired scene, as well as to attribute spectral characteristics to each surface in the scene I will also conduct a systems analysis to determine the interaction and relative importance of various techniques in creating the final scene