Pith. sign in

REVIEW 4 cited by

HoloLens 2 Research Mode as a Tool for Computer Vision Research

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2008.11239 v1 pith:DK36A7XO submitted 2020-08-25 cs.CV

classification cs.CV
keywords researchhololensmodecomputersensorvisioncapabilitiesdata
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Mixed reality headsets, such as the Microsoft HoloLens 2, are powerful sensing devices with integrated compute capabilities, which makes it an ideal platform for computer vision research. In this technical report, we present HoloLens 2 Research Mode, an API and a set of tools enabling access to the raw sensor streams. We provide an overview of the API and explain how it can be used to build mixed reality applications based on processing sensor data. We also show how to combine the Research Mode sensor data with the built-in eye and hand tracking capabilities provided by HoloLens 2. By releasing the Research Mode API and a set of open-source tools, we aim to foster further research in the fields of computer vision as well as robotics and encourage contributions from the research community.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 118 citations worldwide. Full citation record

  1. EPFL-Smart-Kitchen-30: Densely annotated cooking dataset with 3D kinematics to challenge video and language models

    cs.CV 2025-06 conditional novelty 7.0 of 10

    EPFL-Smart-Kitchen-30 is a 29.7-hour multimodal cooking dataset with 60k action segments and four benchmarks, including a kinematic-focused VQA benchmark that shows current video-language models struggle with hand and...

  2. Siren Song: Manipulating Pose Estimation in XR Headsets Using Acoustic Attacks

    cs.CR 2025-02 conditional novelty 7.0 of 10

    Loud tones near the HoloLens 2 IMU resonant frequency reset its pose estimate to the origin, enabling four proof-of-concept AR attacks: input manipulation, clickjacking, denial of interaction, and zone invasion.

  3. What to Distinguish and How? Opportunities and Challenges of Augmenting Multiple, Cluttered Objects in Complex Scenes for People with Low Vision

    cs.HC 2026-07 conditional novelty 6.0 of 10

    For people with low vision, AR overlays that rank objects by importance redirect attention toward high-priority objects, but multi-object augmentation lowers overall scene recall and creates new visual-confusion problems.

  4. Real-Time Kinematic Positioning and Optical See-Through Head-Mounted Display for Outdoor Tracking: Hybrid System and Preliminary Assessment

    cs.HC 2025-09 conditional novelty 4.0 of 10

    A hybrid RTK plus optical see-through HMD system tracks an outdoor UGV with an average error of 0.745 m, beating iPhone GPS by 8.16 m in a preliminary urban test.

Pith tools