Abstract

We extend our instant NeRF implementation [Müller et al. 2022] to allow training from an incremental stream of images and camera poses, provided by a realtime Simultaneous Localization And Mapping (SLAM) system. Camera poses are refined end-to-end by back-propagating the gradients from NeRF training. Reconstruction quality is further improved by compensating for various camera properties, such as rolling shutter, non-linear lens distortion, and variable exposure typical of digital cameras.

Keywords:
Instant Computer science Computer vision Artificial intelligence Radiance Distortion (music) Digital camera Lens (geology) Shutter Computer graphics (images) Remote sensing Optics Geology Physics

Metrics

11
Cited By
1.36
FWCI (Field Weighted Citation Impact)
6
Refs
0.79
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Optical measurement and interference techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Robotics and Sensor-Based Localization
Physical Sciences →  Engineering →  Aerospace Engineering

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