We present a method that accelerates reconstruction of 3D scenes and objects, aiming to enable instant reconstruction on edge devices such as mobile phones and AR/VR headsets. While recent works have accelerated scene reconstruction training to minute/second-level on high-end GPUs, there is still a large gap to the goal of instant training on edge devices which is yet highly desired in many emerging applications such as immersive AR/VR. To this end, this work aims to further accelerate training by leveraging geometry priors of the target scene. Our method proposes strategies to alleviate the noise of the imperfect geometry priors to accelerate the training speed on top of the highly optimized Instant-NGP. On the NeRF Synthetic dataset, our work uses half of the training iterations to reach an average test PSNR of >30.
View on arXiv@article{li2025_2212.01959,
title={ INGeo: Accelerating Instant Neural Scene Reconstruction with Noisy Geometry Priors },
author={ Chaojian Li and Bichen Wu and Albert Pumarola and Peizhao Zhang and Yingyan Celine Lin and Peter Vajda },
journal={arXiv preprint arXiv:2212.01959},
year={ 2025 }
}