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A Search-Classify Approach for Cluttered Indoor Scene Understanding

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A Search-Classify Approach for Cluttered Indoor Scene Understanding. Liangliang Nan 1 , Ke Xie 1 , Andrei Sharf 2. 1 SIAT , China 2 Ben Gurion University, Israel . Digitalization of indoor scenes. Indoor scenes from Google 3D Warehouse. Acquisition of indoor scenes. Goal.

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slide1
A Search-Classify Approach for Cluttered Indoor Scene Understanding

Liangliang Nan1, Ke Xie1, Andrei Sharf2

1 SIAT, China

2 Ben Gurion University, Israel

slide2
Digitalization of indoor scenes

Indoor scenes from Google 3D Warehouse

slide4
Goal
  • Scene understanding
challenges
Challenges
  • Clutter
    • Densely populated
    • Arbitrary arrangements
    • Partial representation
    • Occlusions
  • Complex geometry
classification segmentation
Classification & Segmentation
  • Two interleaved problems
    • What are the objects?
    • Where are the objects?
  • Chicken-egg problem
    • Classification needs segmentation
    • Segmentation needs a prior
our solution
Our solution
  • Search
    • Propagate / accumulate patches
  • Classify
    • Query classifier to detect object
related work
Related Work
      • Indoor scenes (This Session)
    • [Fisher et al. 2012] [Shao et al. 2012] [Kim et al. 2012]
      • Semantic relationship
    • [Fisher et al. 2010, 2011]
  • Recognition using depth + texture (RGB-D)
    • [Quigley et al.2009], [Lai and Fox 2010]
      • Outdoor classification
    • [Golovinskiyet al. 2009]
      • Semantic labeling
    • [Koppulaet al. 2011]

Controlled region growing process

our search classify idea
Our search-classify idea

0.6

0.8

0.92

0.94

0.94

0.94

method o verview
Method overview

Training

Search-Classify

point cloud features
Point cloud features
  • Height-size ratio of BBox
  • Aspect ratio of each layer
  • Bottom-top, mid-top size ratio
  • Change in COM along horizontal slabs

Bh

Bd

Bw

classifier
Classifier
  • Handle missing data
    • Occlusion
  • Random decision forest
    • Efficient multi-class classifier
  • Trained with both scanned and synthetic data
    • Manually segmented and labeled
    • 510 chairs
    • 250 tables
    • 110 cabinets
    • 40 monitors etc.

[Shottonet al. 2008, 2011]

search classify
Search-Classify
  • Starts from seeds
    • Random patch triplets
    • Remove seeds with low confidence
  • Accumulating neighbor patches
    • Highest classification confidence
  • Stop condition
    • Steep decrease in classification confidence

0.65

0.92

0.93

0.88

Seed

slide14
Segmentation refinement by template fitting
  • Segmented - classified objects problems
    • Overlap, outliers, ambiguities etc.
  • Refinement
    • Outliers = patches with large distance
template deformation
Template deformation
  • Different styles for each class
  • Predefined scalable parts
  • Templates can deform

[Xuet al. 2010]

template deformation1
Template deformation
  • Different styles for each class
  • Predefined scalable parts
  • Templates can deform

[Xuet al. 2010]

fitting via template deformation
Fitting via template deformation
  • Best matching template
    • One-side Euclidean distance from points to template

Confidence

Fitting error

Best fitting

results and discussion2
Results and discussion
  • Scalability test with varied object density

0 (25) 1 (45) 5 (60)

results and discussion3
Results and discussion

Lai et al. 2011

  • Comparison

Ours

limitation
Limitation
  • Upward assumption
    • Features
    • Template fitting
future work
Future work
  • Contextual information
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