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Enhancing 3D Object Matching with Robust Segmentation Techniques

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This paper presents a novel approach to 3D object matching by exploiting segmentation to improve robustness against clutter and occlusion. We introduce advanced shape-based pose quality metrics that do not assume any object texture, along with a gradient-based optimization framework for beam-based sensor models. Key contributions include a new segmentation sensor model that enhances the traditional beam-based models by considering viewpoint and surface extents. Results demonstrate improved performance over traditional methods, with all code and test data made freely available for further research.

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Enhancing 3D Object Matching with Robust Segmentation Techniques

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  1. Exploiting Segmentation for Robust 3D Object Matching Michael Krainin, Kurt Konolige, and Dieter Fox

  2. 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

  3. 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

  4. 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

  5. Beam-Based Sensor Models x x x x x p(pixel | model, pose) x x l 0 MeasuredDepth – RenderedDepth Maximize data likelihood: Typically assume pixel independence:

  6. 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

  7. Beam Model Optimization observed rendered ICP Beam 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

  8. Surface Extents Can still match to the wrong surface Idea: Use size of surfaces to rule out matches like these

  9. Segmentation (Over-)Segmentation as an estimate of surface extents Connected components using depths and normals

  10. Segmentation Sensor Model Usual beam-based sensor model: Segmentation model: Allows consistent classification of entire segment as being generated from the model or not

  11. 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

  12. Error Functions x Standard Beam-Based Model Segmentation Model

  13. Test Data Recorded 46 cluttered scenes with ground truth poses

  14. Pose Estimation Results Optimization algorithms: Random restart evaluation functions (upper bounded by 93% from previous result):

  15. Robustness to Segmentation Granularity Spam 1 of 5 0 degrees x x x (Fixed to 8 degrees in other experiments) 12 degrees 8 degrees

  16. 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

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