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HoloLens 2 Sensor Streaming

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arxiv 2211.02648 v1 pith:AQAJKEYW submitted 2022-11-04 cs.MM

Juan C. Dibene , Enrique Dunn This is my paper · ORCID
classification cs.MM
keywords datadevicehololensserverstreamingtrackingcameracapabilities
verification ladder T0 review T1 audit T2 compute T3 formal
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We present a HoloLens 2 server application for streaming device data via TCP in real time. The server can stream data from the four grayscale cameras, depth sensor, IMU, front RGB camera, microphone, head tracking, eye tracking, and hand tracking. Each sent data frame has a timestamp and, optionally, the instantaneous pose of the device in 3D space. The server allows downloading device calibration data, such as camera intrinsics, and can be integrated into Unity projects as a plugin, with support for basic upstream capabilities. To achieve real time video streaming at full frame rate, we leverage the video encoding capabilities of the HoloLens 2. Finally, we present a Python library for receiving and decoding the data, which includes utilities that facilitate passing the data to other libraries. The source code, Python demos, and precompiled binaries are available at https://github.com/jdibenes/hl2ss.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. 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.

  2. 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.

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