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HDRFusion: HDR SLAM using a low-cost auto-exposure RGB-D sensor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2016 Fourth International Conference on 3D Vision (3DV 2016)
Subtitle of host publicationProceedings of a meeting held 25-28 October 2016, Stanford, CA, USA
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Electronic)9781509054077
ISBN (Print)9781509054084
DateAccepted/In press - 2 Sep 2016
DatePublished (current) - 19 Dec 2016
Event2016 International Conference on 3D Vision - University of Stanford, California, United States
Duration: 25 Oct 201628 Oct 2016


Conference2016 International Conference on 3D Vision
Abbreviated title3D Vision 2016
CountryUnited States
Internet address


Most dense RGB/RGB-D SLAM systems require the brightness of 3-D points observed from different viewpoints to be constant. However, in reality, this assumption is dif- ficult to meet even when the surface is Lambertian and il- lumination is static. One cause is that most cameras auto- matically tune exposure to adapt to the wide dynamic range of scene radiance, violating the brightness assumption. We describe a novel system - HDRFusion - which turns this ap- parent drawback into an advantage by fusing LDR frames into an HDR textured volume using a standard RGB-D sen- sor with auto-exposure (AE) enabled. The key contribution is the use of a normalised metric for frame alignment which is invariant to changes in exposure time. This enables robust tracking in frame-to-model mode and also compensates the exposure accurately so that HDR texture, free of artefacts, can be generated online. We demonstrate that the track- ing robustness and accuracy is greatly improved by the ap- proach and that radiance maps can be generated with far greater dynamic range of scene radiance.

    Research areas

  • high dynamic range, 3-D mapping and tracking, auto exposure, RGB-D cameras


2016 International Conference on 3D Vision

Abbreviated title3D Vision 2016
Duration25 Oct 201628 Oct 2016
Location of eventUniversity of Stanford
CountryUnited States
Web address (URL)
Degree of recognitionInternational event

Event: Conference

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the accepted author manuscript (AAM). The final published version (version of record) is available online via IEEE at Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 7 MB, PDF-document


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