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.
翻译:我们提出了一个加速重建3D场景和物体的方法,目的是在诸如移动电话和AR/VR头盔等边缘装置上进行即时重建。虽然最近的工程加快了现场重建培训,将高端GPP的速/秒培训加速到中/秒水平,但在边缘装置的即时培训目标方面仍然存在巨大差距,在诸如浸泡式AR/VR等许多新兴应用中,这种目标仍然非常可取。为此,这项工作旨在通过利用目标场景的几何前程来进一步加快培训。我们的方法提出了减轻不完善前几何学噪音的战略,以便在高度优化的实时-NGP之上加速培训速度。关于NERF合成数据集,我们的工作利用一半的培训迭代来达到平均测试大于30的PSNR。