Motion-Based Object Segmentation Based on Dense RGB-D Scene Flow

Given two consecutive RGB-D images, we propose a model that estimates a dense three-dimensional (3D) motion field, also known as scene flow. We take advantage of the fact that in robot manipulation scenarios, scenes often consist of a set of rigidly moving objects. Our model jointly estimates the fo...

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Vydané v:IEEE robotics and automation letters Ročník 3; číslo 4; s. 3797 - 3804
Hlavní autori: Lin Shao, Shah, Parth, Dwaracherla, Vikranth, Bohg, Jeannette
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.10.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Artificial neural networks
Computer simulation
Decoding
deep learning in robotics and automation
Estimation
Image compression
Image segmentation
Motion segmentation
object detection
Object motion
Object segmentation
Qualitative analysis
RGB-D perception
segmentation and categorization
Three dimensional models
Three dimensional motion
Three-dimensional displays
Trajectory
ISSN:2377-3766, 2377-3766
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Popis
Shrnutí:Given two consecutive RGB-D images, we propose a model that estimates a dense three-dimensional (3D) motion field, also known as scene flow. We take advantage of the fact that in robot manipulation scenarios, scenes often consist of a set of rigidly moving objects. Our model jointly estimates the following: First, the segmentation of the scene into an unknown but finite number of objects, second, the motion trajectories of these objects, and finally, the object scene flow. We employ an hourglass, deep neural network architecture. In the encoding stage, the RGB and depth images undergo spatial compression and correlation. In the decoding stage, the model outputs three images containing a per-pixel estimate of the corresponding object center as well as object translation and rotation. This forms the basis for inferring the object segmentation and final object scene flow. To evaluate our model, we generated a new and challenging, large scale, synthetic dataset that is specifically targeted at robotic manipulation: It contains a large number of scenes with a very diverse set of simultaneously moving 3D objects and is recorded with a simulated, static RGB-D camera. In quantitative experiments, we show that we outperform state-of-the-art scene flow and motion-segmentation methods on this data set. In qualitative experiments, we show how our learned model transfers to challenging real-world scenes, visually generating better results than existing methods.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2018.2856525