The use of Neural Radiance Fields (NeRFs) for 3D reconstruction is on the rise, but the challenge of obtaining a suitable dataset for NeRFs persists. This difficulty arises from the need for a collection of images whose positions and calibration parameters are accurately determined. NeRFCapture addresses this challenge by leveraging ARKit to establish the pose of each captured image. The resulting dataset, containing images and their respective poses and calibration parameters, can be saved offline or shared directly with your preferred NeRF implementation over the network. NeRFCapture seamlessly integrates with InstantNGP, a popular NeRF implementation, and allows for real-time data transfer using DDS with automatic peer discovery. Furthermore, NeRFCapture can also broadcast depth data if your iOS device is equipped with LiDAR. Simplify your data capture process with NeRFCapture (https://github.com/jc211/NeRFCapture), an open-source tool that encourages contributions from the community. For detailed instructions on using InstantNGP with NeRFCapture, please refer to https://github.com/NVlabs/instant-ngp/blob/master/docs/nerf_dataset_tips.md#NeRFCapture.