Learned Semantic Multi-Sensor Depth Map Fusion
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00338337" target="_blank" >RIV/68407700:21230/19:00338337 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICCVW.2019.00264" target="_blank" >https://doi.org/10.1109/ICCVW.2019.00264</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCVW.2019.00264" target="_blank" >10.1109/ICCVW.2019.00264</a>
Alternative languages
Result language
angličtina
Original language name
Learned Semantic Multi-Sensor Depth Map Fusion
Original language description
Volumetric depth map fusion based on truncated signed distance functions has become a standard method and is used in many 3D reconstruction pipelines. In this paper, we are generalizing this classic method in multiple ways: 1) Semantics: Semantic information enriches the scene representation and is incorporated into the fusion process. 2) Multi-Sensor: Depth information can originate from different sensors or algorithms with very different noise and outlier statistics which are considered during data fusion. 3) Scene denoising and completion: Sensors can fail to recover depth for certain materials and light conditions, or data is missing due to occlusions. Our method denoises the geometry, closes holes and computes a watertight surface for every semantic class. 4) Learning: We propose a neural network reconstruction method that unifies all these properties within a single powerful framework. Our method learns sensor or algorithm properties jointly with semantic depth fusion and scene completion and can also be used as an expert system, e.g. to unify the strengths of various photometric stereo algorithms. Our approach is the first to unify all these properties. Experimental evaluations on both synthetic and real data sets demonstrate clear improvements.
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
<a href="/en/project/GA18-05360S" target="_blank" >GA18-05360S: Solving inverse problems for the analysis of fast moving objects</a><br>
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 Computer Vision Workshops (ICCVW 2019)
ISBN
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ISSN
2473-9944
e-ISSN
2473-9944
Number of pages
11
Pages from-to
2089-2099
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Seoul
Event date
Oct 27, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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