Combining HoloLens with Instant-NeRFs: Advanced Real-Time 3D Mobile Mapping
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Abstract (arXiv)
This work represents a large step into modern ways of fast 3D reconstruction based on RGB camera images. Utilizing a Microsoft HoloLens 2 as a multisensor platform that includes an RGB camera and an inertial measurement unit for SLAM-based camera-pose determination, we train a Neural Radiance Field (NeRF) as a neural scene representation in real-time with the acquired data from the HoloLens. The HoloLens is connected via Wifi to a high-performance PC that is responsible for the training and 3D reconstruction. After the data stream ends, the training is stopped and the 3D reconstruction is initiated, which extracts a point cloud of the scene. With our specialized inference algorithm, five million scene points can be extracted within 1 second. In addition, the point cloud also includes radiometry per point. Our method of 3D reconstruction outperforms grid point sampling with NeRFs by multiple orders of magnitude and can be regarded as a complete real-time 3D reconstruction method in a mobile mapping setup.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 41 references that are in the map. First posted on arXiv 27 Apr 2023.
Builds on
22 of 41 references.
References · 22
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10 more
- 2021Urban Radiance Fieldscited by 263
- 2021Self-Calibrating Neural Radiance Fieldscited by 204
- 2021Deblur-NeRF: Neural Radiance Fields from Blurry Imagescited by 179
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Citing papers per year: 2023: 1.
1 paper.
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