Fast and Extensible Building Modeling from Airborne LiDAR Data

1
Fast and Extensible Building Modeling from
Airborne LiDAR Data

• Qian-Yi Zhou Ulrich Neumann
• University of Southern California

2
Introduction

• Toward automatic 3D building model reconstruction
  
• 3D models are useful in several applications
• Manual creation is slow and expensive
• New instruments provides more and more data
• Problem

How to fulfill the gap between data and 3D
building models?
3
Introduction

• Approach overview
• Data source airborne LiDAR data
• Without other data sources
• Working directly on point cloud, without
  rasterization
• Experiments
• On 3 different data sets
• Automatic vs. interaction
• Fully automatic for flat roofs
• Use interaction to acquire the extensibility for
  non-flat roof patterns

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Pipeline
5
Pipeline
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Classification

• Algorithm
• Use a SVM (Support Vector Machine) algorithm
• Machine learning method
• All the weight parameters could be trained from
  a small area of labeled data
• Take several differential geometry properties as
  features
• Features are defined locally
• Same solution works for different data sets, even
  their global variants (e.g. absolute height)
  varies

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Classification

• Features Building (ground) vs. vegetation
• Distribution of neighbor points
• Regular vs. Irregular
• Normal direction
• Vertical vs. Unordered
• Flatness
• Flat vs. Non-flat
• Normal distribution
• Regular vs. Irregular

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Classification
0.0
1.0
0.5
Input LiDAR data
F1 (regularity of distribution)
F2 (normal direction)
F3 (flatness)
F4 (regularity of normal dist.)
F5 (regularity of normal dist.)
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Classification

• Experiments for classification
• Weights for features learned from data
• W15 (2.5, 0.1, 1.7, 5.2, 20.4)
• Regularities of normal distribution are important
• We get around 95 accuracy rate on 3 different
  data sets
• Sample rate of different data sets varies from 6
  points per sq.m. to 17 points per sq.m.
• Global variants (e.g. average height, average
  intensity) varies between different data sets

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Classification

• Post-processing
• Refinement
• Use the intuition that points of same category
  usually occur together in space
• Let points in a local neighborhood vote for the
  final classification result
• Segmentation
• Apply region growing algorithm to find roof
  patches
• Largest patch is assigned as ground

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Classification
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Pipeline
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Plane extraction

• Algorithm
• For each roof patch, apply a region growing
  algorithm but based on normal similarity
• Iteratively find all plane patterns in one roof
  patch

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Pipeline
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Boundary detection

• Algorithm
• Apply a uniform grid onto the point cloud P
• Correspondence
• Boundary line in grid ? Boundary point of P
• Boundary corner in grid ? Boundary edge of P
• Run two passes, to construct a watertight
  manifold polygonal boundary from boundary points
  and edges

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Boundary detection

• Algorithm (continue)

Boundary corners
Boundary lines
First pass
Second pass
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Boundary detection

• Advantages
• Topology guarantee watertight manifold boundary
• Easy to implement
• Efficient and robust
• Can be used withmorphological operations
• Limitation
• Cannot guarantee geometry completeness

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Pipeline
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Building modeling

• Principal directions
• Intuition most boundary line segments in a local
  area fall into a couple of directions, known as
  principal directions

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7
2
3
1
4
5
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Building modeling

• Snapping
• Snap to principal directions
• Find as much as possible boundary segments along
  principal directions
• Snap between neighbor segments
• Patches may share same boundary segments
• Patches may be connected via a vertical wall

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Building modeling
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Pipeline
User interaction
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Non-flat objects extension

• Algorithm
• Define a few pattern types, e.g. cone, sphere,
  cylinder
• Let user specify pattern type of certain roof
  patch
• Apply RANSAC algorithm to get the parameters for
  this shape pattern

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Experiments

• Oakland (17 samples/sq.m. 600m x 600m)

25
Experiments

• Denver (6 samples/sq.m. 1km x 1km)

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Experiments

• Industrial site (14 samples/sq.m.)

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Conclusion

• We provide an automatic building modeling
  pipeline with novel classification, boundary
  extraction and modeling algorithms in addition,
  we show the extensibility to non-flat roofs with
  the help of a few user interactions.
• Future works
• How to quantitatively analysis errors?
• How to handle data sets with billions of points?
• An out-of-core implementation is possible!

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The end

• Q A
• Acknowledgement
• Data
• Airborne 1 Corp., Sanborn Corp., Chevron Corp.,
  Sentinel AVE LLC
• Discussion and comments
• Suya You, Yuan Li, and anonymous reviewers
• Support
• Provosts Fellowship from USC