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Object search -- the problem of finding a target object in a cluttered scene -- is essential to solve for many robotics applications in warehouse and household environments.
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Patrick Min · 2004
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“Boundary extraction in natural images using ultrametric contour maps”
Pablo Arbelaez · 2006
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“Manipulation planning among movable obstacles”
Mike Stilman, Jan Schamburek, James Kuffner and Tamim Asfour · 2007
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“A planning framework for non-prehensile manipulation under clutter and uncertainty”
Mehmet. Dogar and Siddhartha. Srinivasa · 2012
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“Fast object localization and pose estimation in heavy clutter for robotic bin picking”
Ming Liu et al · 2012
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“3D pose estimation of daily objects using an RGB-D camera”
Changhyun Choi and Henrik Christensen · 2012
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“Indoor Segmentation and Support Inference from RGBD Images”
Pushmeet Nathan Derek and Rob Fergus · 2012
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“Manipulation-based active search for occluded objects”
Lawson.S. Wong, Leslie Kaelbling and Tomas Lozano-Perez · 2013
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“OctoMap: an efficient probabilistic 3D mapping framework based on octrees”
Armin Hornung et al · 2013
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“Object search by manipulation”
Mehmet. Dogar, Michael. Koval, Abhijeet Tallavajhula and Siddhartha. Srinivasa · 2014
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“Generative adversarial nets”
Ian Goodfellow et al · 2014
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“The YCB object and model set: Towards common benchmarks for manipulation research”
Berk Calli et al · 2015
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“Leveraging big data for grasp planning”
Daniel Kappler, Jeannette Bohg and Stefan Schaal · 2015
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“ShapeNet: An Information-Rich 3D Model Repository”, 2015
Angel. Chang et al · 2015
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“Using Manipulation Primitives for Object Sorting in Cluttered Environments”
Megha Gupta, Jörg Müller and Gaurav. Sukhatme · 2015
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“Learning to Manipulate Unknown Objects in Clutter by Reinforcement”
Abdeslam Boularias, J. Bagnell and Anthony Stentz · 2015
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“3D Object Reconstruction from a Single Depth View with Adversarial Learning”
Bo Yang et al · 2017
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“Semantic scene completion from a single depth image”
Shuran Song et al · 2017
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“A Point Set Generation Network for 3D Object Reconstruction from a Single Image.”
Haoqiang Fan, Hao Su and Leonidas Guibas · 2017
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“Mask r-cnn”
Kaiming He, Georgia Gkioxari, Piotr Dollár and Ross Girshick · 2017
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“RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation”
G. Lin, A. Milan, C. Shen and I. Reid · 2017
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“Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks”
K.K. Maninis, J. Pont-Tuset, P. Arbeláez and L. Gool · 2017
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“Probabilistic multi-class segmentation for the amazon picking challenge”
Rico Jonschkowski et al · 2016
Cited alongside, same era.
“Act to see and see to act: POMDP planning for objects search in clutter”
Jue Li, David Hsu and Wee Lee · 2016
Cited alongside, same era.
“Structured prediction of unobserved voxels from a single depth image”
Michael Firman, Oisin Mac, Simon Julier and Gabriel Brostow · 2016
Cited alongside, same era.
“NimbRo Picking: Versatile part handling for warehouse automation”
Max Schwarz et al · 2017
Cited alongside, same era.
“Shape completion enabled robotic grasping”
Jacob Varley et al · 2017
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“Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching”
Andy Zeng et al · 2018
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“Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene”
Shubham Tulsiani et al · 2018
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“PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes”
Yu Xiang, Tanner Schmidt, Venkatraman Narayanan and Dieter Fox · 2018
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“Dense 3D Object Reconstruction from a Single Depth View”
Bo Yang et al · 2018
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“SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes”
T. Pham, T.-T. Do, N. Sünderhauf and I. Reid · 2018
Later among the works it cites.