Combining HoloLens with Instant-NeRFs: Advanced Real-Time 3D Mobile Mapping

• URL: http://arxiv.org/abs/2304.14301v2
• Date: Wed, 3 May 2023 11:18:29 GMT
• Title: Combining HoloLens with Instant-NeRFs: Advanced Real-Time 3D Mobile Mapping
• Authors: Dennis Haitz, Boris Jutzi, Markus Ulrich, Miriam Jaeger, Patrick Huebner
• Abstract summary: We train a Neural Radiance Field (NeRF) as a neural scene representation in real-time with the acquired data from the HoloLens. After the data stream ends, the training is stopped and the 3D reconstruction is initiated, which extracts a point cloud of the scene. Our method of 3D reconstruction outperforms grid point sampling with NeRFs by multiple orders of magnitude.
• Score: 4.619828919345114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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