2017

PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

Xiang, Yu, Schmidt, Tanner, Narayanan, Venkatraman et al.

Understand

Estimating the 6D pose of known objects is important for robots to interact with the real world.

The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects.
In this work, we introduce PoseCNN, a new Convolutional Neural Network for 6D object pose estimation.
PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera.

Built on

Object recognition from local scale-invariant features
David G Lowe · 1999
Earlier work this paper cites.
Combined object categorization and segmentation with an implicit shape model
Bastian Leibe, Ales Leonardis, and Bernt Schiele · 2004
Earlier work this paper cites.
3D object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints
Fred Rothganger, Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce · 2006
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
The MOPED framework: Object recognition and pose estimation for manipulation
Alvaro Collet, Manuel Martinez, and Siddhartha S Srinivasa · 2011
Earlier work this paper cites.
Fast Hough transform on GPUs: Exploration of algorithm trade-offs
Gert-Jan van den Braak, Cedric Nugteren, Bart Mesman, and Henk Corporaal · 2011
Earlier work this paper cites.
Learning hierarchical sparse features for RGB-D object recognition
Liefeng Bo, Xiaofeng Ren, and Dieter Fox · 2014
Earlier work this paper cites.
Learning 6D object pose estimation using 3D object coordinates
Eric Brachmann, Alexander Krull, Frank Michel, Stefan Gumhold, Jamie Shotton, and Carsten Rother · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Original
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
The YCB object and model set: Towards common benchmarks for manipulation research
Berk Calli, Arjun Singh, Aaron Walsman, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M Dollar · 2015
Earlier work this paper cites.
Fast R-CNN
Ross Girshick · 2015
Earlier work this paper cites.

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