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Reuben Lindroos

dblp:241/5411 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Computational photography and imaging · 50% Virtual and augmented reality · 50%
Artificial intelligence
1 paper
3D vision · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational photography and imaging › panoramic imaging
360° panoramas
0.412020
OmniPhotos: casual 360° VR photography · ACM Trans. Graph. 2020
Virtual and augmented reality › depth perception
motion parallax
0.412020
OmniPhotos: casual 360° VR photography · ACM Trans. Graph. 2020
Computer vision › 3D vision
3d reconstruction
0.112020
OmniPhotos: casual 360° VR photography · ACM Trans. Graph. 2020
YearPublicationVenuePosition
2020 Arbitrarily large tomography with iterative algorithms on multiple GPUs using the TIGRE toolbox
Ander Biguri, Reuben Lindroos, Robert Bryll, Hossein Towsyfyan, Hans Deyhle, Ibrahim El Khalil Harrane, Richard P. Boardman, Mark Mavrogordato, Manjit Dosanjh, Steven Hancock, Thomas Blumensath
J. Parallel Distributed Comput.2
2020 OmniPhotos: casual 360° VR photography
abstract
Virtual reality headsets are becoming increasingly popular, yet it remains difficult for casual users to capture immersive 360° VR panoramas. State-of-the-art approaches require capture times of usually far more than a minute and are often limited in their supported range of head motion. We introduce OmniPhotos, a novel approach for quickly and casually capturing high-quality 360° panoramas with motion parallax. Our approach requires a single sweep with a consumer 360° video camera as input, which takes less than 3 seconds to capture with a rotating selfie stick or 10 seconds handheld. This is the fastest capture time for any VR photography approach supporting motion parallax by an order of magnitude. We improve the visual rendering quality of our OmniPhotos by alleviating vertical distortion using a novel deformable proxy geometry, which we fit to a sparse 3D reconstruction of captured scenes. In addition, the 360° input views significantly expand the available viewing area, and thus the range of motion, compared to previous approaches. We have captured more than 50 OmniPhotos and show video results for a large variety of scenes. We will make our code available.
Tobias Bertel, Mingze Yuan, Reuben Lindroos, Christian Richardt
ACM Trans. Graph.3