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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • 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