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Segmenting Unseen Industrial Components In A Heavy Clutter Using RGB-D Fusion And Synthetic Data

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Citation Author(s):
Jongwon Kim, Raeyoung Kang, Seungjun Choi, Kyoobin Lee
Submitted by:
Seunghyeok Back
Last updated:
15 November 2020 - 3:24am
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Seunghyeok Back
Paper Code:
3032

Abstract 

Abstract: 

Segmentation of unseen industrial parts is essential for autonomous industrial systems. However, industrial components are texture-less, reflective, and often found in cluttered and unstructured environments with heavy occlusion, which makes it more challenging to deal with unseen objects. To tackle this problem, we present a synthetic data generation pipeline that randomizes textures via domain randomization to focus on the shape information. In addition, we propose an RGB-D Fusion Mask R-CNN with a confidence map estimator, which exploits reliable depth information in multiple feature levels. We transferred the trained model to real-world scenarios and evaluated its performance by making comparisons with baselines and ablation studies. We demonstrate that our methods, which use only synthetic data, could be effective solutions for unseen industrial components segmentation.

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PresentationSlides-ICIP2020-Segmenting Unseen Industrial Components In A Heavy Clutter Using RGB-D Fusion And Synthetic Data.pdf

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