EPOS: Estimating 6D Pose of Objects with Symmetries

Autor: Jiri Matas, Tomas Hodan, Daniel Barath
Jazyk: angličtina
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
0209 industrial biotechnology
Computer Science - Machine Learning
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
Machine Learning (cs.LG)
Computer Science - Robotics
020901 industrial engineering & automation
Robustness (computer science)
FOS: Electrical engineering
electronic engineering
information engineering
0202 electrical engineering
electronic engineering
information engineering
Pose
Pixel
business.industry
Image and Video Processing (eess.IV)
QA75 Electronic computers. Computer science / számítástechnika
számítógéptudomány
Pattern recognition
Electrical Engineering and Systems Science - Image and Video Processing
Homogeneous space
RGB color model
020201 artificial intelligence & image processing
Artificial intelligence
business
Robotics (cs.RO)
Zdroj: CVPR
Popis: We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image. The method is applicable to a broad range of objects, including challenging ones with global or partial symmetries. An object is represented by compact surface fragments which allow handling symmetries in a systematic manner. Correspondences between densely sampled pixels and the fragments are predicted using an encoder-decoder network. At each pixel, the network predicts: (i) the probability of each object's presence, (ii) the probability of the fragments given the object's presence, and (iii) the precise 3D location on each fragment. A data-dependent number of corresponding 3D locations is selected per pixel, and poses of possibly multiple object instances are estimated using a robust and efficient variant of the PnP-RANSAC algorithm. In the BOP Challenge 2019, the method outperforms all RGB and most RGB-D and D methods on the T-LESS and LM-O datasets. On the YCB-V dataset, it is superior to all competitors, with a large margin over the second-best RGB method. Source code is at: cmp.felk.cvut.cz/epos.
Accepted to CVPR 2020
Databáze: OpenAIRE
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