Photorealistic Image Synthesis for Object Instance Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00328593" target="_blank" >RIV/68407700:21230/19:00328593 - isvavai.cz</a>
Result on the web
<a href="https://arxiv.org/abs/1902.03334" target="_blank" >https://arxiv.org/abs/1902.03334</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICIP.2019.8803821" target="_blank" >10.1109/ICIP.2019.8803821</a>
Alternative languages
Result language
angličtina
Original language name
Photorealistic Image Synthesis for Object Instance Detection
Original language description
We present an approach to synthesize highly photorealistic images of 3D object models, which we use to train a convolutional neural network for detecting the objects in real images. The proposed approach has three key ingredients: (1) 3D object models are rendered in 3D models of complete scenes with realistic materials and lighting, (2) plausible geometric configuration of objects and cameras in a scene is generated using physics simulations, and (3) high photorealism of the synthesized images achieved by physically based rendering. When trained on images synthesized by the proposed approach, the Faster R-CNN object detector achieves a 24% absolute improvement of mAP@.75IoU on Rutgers APC and 11% on LineMod-Occluded datasets, compared to a baseline where the training images are synthesized by rendering object models on top of random photographs. This work is a step towards being able to effectively train object detectors without capturing or annotating any real images. A dataset of 600K synthetic images with ground truth annotations for various computer vision tasks will be released on the project website: thodan.github.io/objectsynth.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2019 IEEE International Conference on Image Processing (ICIP)
ISBN
978-1-5386-6249-6
ISSN
1522-4880
e-ISSN
2381-8549
Number of pages
5
Pages from-to
66-70
Publisher name
IEEE
Place of publication
Piscataway, NJ
Event location
Taipei
Event date
Sep 22, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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