Exploiting Segmentation for Robust 3D Object Matching Michael Krainin, Kurt Konolige, and Dieter...
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Transcript of Exploiting Segmentation for Robust 3D Object Matching Michael Krainin, Kurt Konolige, and Dieter...
Exploiting Segmentation for Robust 3D Object Matching Michael Krainin, Kurt Konolige, and Dieter Fox
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Shape-based Pose Quality Metrics
Given an object model (triangle mesh) and a depth map,
Should not assume any object texture Should be robust to clutter and occlusion
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Outline
Progression of quality metrics Iterative Closest Point Beam-based Sensor Models Segmentation Sensor Model
Key Contributions Gradient-based optimization for beam-based models
Improves on ICP by reasoning about free-space Novel sensor model framework
Uses segmentation to relax beam-independence assumptions
Explicitly reasons about surface extents
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ICP Error Metric
Mean squared distance for pairs of closest points [Besl & McKay‘92]
No notion of amount of model or scene explained by the correspondences
Discards viewpoint/free-space information
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x xx
Beam-Based Sensor Models
Maximize data likelihood:
Typically assume pixel independence:
Measured Depth – Rendered Depth0l
p(p
ixel |
mod
el, p
ose
)
x
x
x
x
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Beam Model Sensor Models
Beam-based sensor model properties: Consider viewpoint Prefer to explain more pixels using the model
Use in pose estimation: 2D particle-based localization [Thrun et al. ‘05]
Coarse to fine grid search for body tracking [Ganapathi et al. ‘10]
Annealed particle filters for vehicle detection and tracking [Petrovskaya & Thrun ‘09]
Has not been used with gradient-based optimization
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Beam Model Optimization
We propose a gradient-based optimization
Levenberg-Marquardt using the g2o graph optimization framework [Kümmerle et al. ‘11]
Error function evaluations by re-rendering OpenGL rendering, CUDA sensor model
evaluation 1 ms per evaluation on a mid-range graphics
card ~500 evaluations per g2o optimization
ICP
Beam Optimization
observed rendered
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Surface Extents
Can still match to the wrong surface
Idea: Use size of surfaces to rule out matches like these
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Segmentation
(Over-)Segmentation as an estimate of surface extents
Connected components using depths and normals
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Segmentation Sensor Model
Usual beam-based sensor model:
Segmentation model:
Allows consistent classification of entire segment as being generated from the model or not
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Segmentation Sensor Model
Let S be a partition (segmentation) of D. Then given model M and pose T,
Let mi be an indicator for whether Si was generated from M
Segment pixels conditionally independent given classification
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Error Functions
Standard Beam-Based Model Segmentation Model
x
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Test Data
Recorded 46 cluttered scenes with ground truth poses
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Pose Estimation Results
Optimization algorithms:
Random restart evaluation functions (upper bounded by 93% from previous result):
ICP Beam Optimization
Successful convergence [%] (exists a correct end pose among 20 random restarts)
86 ± 3 93 ± 2
ICP Mean Squared Error
Standard Beam Model
Segmentation Beam Model
Successful matches [%](determines correct end pose among 20 restarts)
79 ± 3 67 ± 4 85 ± 3
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Robustness to Segmentation Granularity
Spam 1 of 5 0 degrees
8 degrees 12 degrees
x
x
x
(Fixed to 8 degrees in other experiments)
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Conclusions
Contributions Novel formulation for beam-based sensor models Demonstration of gradient-based approach for optimizing
beam-based sensor models All code and test data freely available
Future Work Detecting failed segmentations Extensions such as color and normal direction Directly optimizing the segmentation model